CA2194429C - System and method for geomagnetic and ionospheric forecasting - Google Patents
System and method for geomagnetic and ionospheric forecasting Download PDFInfo
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Abstract
A system and method forecast geomagnetic events and resulting currents from ground and space weather data, including solar wind velocity data and interplanetary magnetic field data. The system has a processor including a first prediction generator for predicting a midnight equatorial boundary (MEB) value; a second prediction generator for predicting a polar cap potential (PCP) value from the ground and space weather data; an AL and AU
prediction generator for predicting AL and AU values; a Kp value generator for generating a Kp-related value; an electric field pattern generator for determining electric field patterns from the Kp value, the PCP value, and the ground and space weather data; a conductivity generator for determining conductivity values from the ground and space weather data and the Kp value; and an adaptive feedback generator for adaptively generating the geomagnetic parameters from the conductivity values, the electric field values, and the predicted AL and AU values. The geomagnetic forecasting system and method forecast geomagnetic parameters and events such as the occurrence of magnetic storms and substorms and their effects on ionospheric currents using ground and space-based measurements.
prediction generator for predicting AL and AU values; a Kp value generator for generating a Kp-related value; an electric field pattern generator for determining electric field patterns from the Kp value, the PCP value, and the ground and space weather data; a conductivity generator for determining conductivity values from the ground and space weather data and the Kp value; and an adaptive feedback generator for adaptively generating the geomagnetic parameters from the conductivity values, the electric field values, and the predicted AL and AU values. The geomagnetic forecasting system and method forecast geomagnetic parameters and events such as the occurrence of magnetic storms and substorms and their effects on ionospheric currents using ground and space-based measurements.
Description
- 21 q442~
~ SYSTEN AND METHOD FOR GF.~M~ ETIC
A~D IONOSPHERIC ~R~CASTING
B~CXGROUND INFO~MATION
1.. Technical Field S This disclosure relates to enYironmehtal forecasting, and in particular to a ~ystQm ~nd me~od ~or forecasting geomagnetic and electr~dynamic parameters and events u6ing ground and space-based measurements.
~ SYSTEN AND METHOD FOR GF.~M~ ETIC
A~D IONOSPHERIC ~R~CASTING
B~CXGROUND INFO~MATION
1.. Technical Field S This disclosure relates to enYironmehtal forecasting, and in particular to a ~ystQm ~nd me~od ~or forecasting geomagnetic and electr~dynamic parameters and events u6ing ground and space-based measurements.
2. Description of ~he Related Art Technological systems in space and on the Earth's sur~ace are subject to adverse e~fects from solar-driven space weather effects. Magnetic storms and substorms impact the distribution and intensity of cu~ren~s in the ionosphere and energetic particle precipitation into the ionosphere. A
visual mani~estation of these effects is the aurora borealis or northern lights. 5uch geomagnetic activity has been known to disrupt communications, degrade navigation sensors, induce currents in long po~er lines and pipelines, etc.
For example, one magnetic storm is known to have induced sufficient currents to disrup~ the entire ~ydro Quebec power ~rid, caus~ng a four hour blackout of all Of Que~ec. The cause of these disturbances is episodic energy and mass releases f~om the Sun. The prediction of the occurren~e of magnetic storms and substorms and ~heir , .
J
.. ~...~
. - :
21 q442q .
660-2.
effects on ionospheric currents may facilitate - implement~tion of mitigating actions to minimize the adverse effects o~ such storms.
Xnown methods ~or ~orec~tlng U9~ g~o~agnetla S indice~ 6~ch as Kp, AE, An, and AL. Geomagnetlc lnd~es ~re derived ~rom qround magnetometer measurements. Variable currents in the ionosphere cause changes in the Ea~th~s ~agnetic field. The AE, AU, and AL are typically instantaneous quantities determined for each ~inute after the fact, as opposed to forecasts. Also, t~e Kp indicator is typically a three hour index, but not a rapidly varying quantity. In a~dition, the AL and AU indices relate to magnitudes of ionospheric currents at auroral latitudes but may not p~ovide information as to the longit~ n~ l or local distri~tion of such currents or the size o~ patterns of currentS. For t~ese reasons, known forecasting based on t~e Xp, AE, ~, and AL indices are t~us limited in accuracy and do not provide location-specific information on the ionosp~eric currents.
One additional index is the daily average 10.? ~.
~lux level of radio emissions fro~ ~he Sun, labelled ~10.?.
: The F10.7 index is commonly used in the art as a proxy for t~e solar ultraviolet (W) ra~iation that is an ionization source for the iono~phere. This ifi determined daily at ~he Ottawa Observatory ~nd is distributed by the NOAA Space Environment Services ~enter (s~sc).
SU~ARY
~he disclosed geomagnetic forecasting system and method provide the capacity for forecasting geomagnetic parameters and events such as the occurrence of magnetic s~orms and substorms and their e~fects on ionospheric currents using prPdictive and tLme-Varyi~g ionospheric and space-based measurements uith improved information on t~e dist~ibution, size, a~d specific location of current pa~terns. ~Such a sys~em and met~od facilitate _. implementation of mitigating actions to rinimi~e t~e effects of fiuch stcrms.
A system and method f or forecasting geomagnetic events from ground and space weather ~a~a, including solar wind velocity data and interplane~a~y magnetic field da~a are disclosed, having a processor including a first prediction generator for predic~ing a midnig~t equatorward ~oundary (M~B) of the diffuse au~ora from the ground and space weather data; a second p~ediction generator for predicting a polar cap potential tPCP) value from the ground ~nd space ~ea~he~ data; an Al and AU prediction generator for predicting AL and A~ val~es from the ground ~nd space weat~er data; ~eans ~or gen~ating a Kp value from the MEB
value; an e~ectric field pattern gene~ator for determining electric field patterns from the Kp value, the PCP v~lue and 5 the ground and space weather data; a conductlvity generator G~ uetermining conductivity values from the fo~ecas~ time, the gr~ d -r,d ~pac~ ~ca~he~ d~ta, and the Kp value, and an adap~ive ~eedback generator for adaptively generating the geomagnetîc pa~ameters from the conductivity v~lues, the IO ele~tric field ~alues, and the predicted ~L and AU values.
BRIEF DESCRI~TTON OP ~E DRAWINGS
The features of the disclosed geomagnetic ~orecasting system and method will become more readily a~parent and may be ~e~ter understood by referring to the Iollowing detailed description of lllustrative embodiments of the presen~ invention, taken in conjunction with the acco~panying d~awings, in whic~:
FIG. 1 is a block diagram of the disclosed geomagnetic forecasting syste~;
FIG. 2 is a flowchart of the method of ope~ation o~ the disclosed geomagnetic forecasting syste~; 1 ~ FIG. ~ is a flowchart for dQtermining solar-W-induce~ con.ductl~iTi'y;
' 219442~
.
~ . ~. FIG. 4 is a flo~chart for the ps~cPs6ing ol input ground and space weathex data;
FI&. 5 is a flowchart for the generation of intermediate pa~meters; and FIG. 6 is a flowchart for the adaptive determination of qeomaqnetic para.meterc a5 o~t~lat ~c~e~s'~.
~SCRIPTION OF THE PREFERRED EMBODIMENTS
Referring now in specific detail to the d~awings, with li~e ~eference numeral6 identifying s~m~lar or identical eleme~ts, as shown in FIG. 1, the present aisciosure deBcribe~ a geomagnetic forecasting system 10 and method for ~orecasting ~eomagnetic parameters and events such as the occurrence of ma~netic storms and substorms and their effects on ionospheric currents using ionospheric and ~pace-based measuXements.
In an illustrative embodiment, the geomagnetic forecasting system 10 receives ground and space ~eather data, including data reflecting activity of the sola~ wind, such as solar ~ind velocity, as well as data measuring 20 interplanet~y magnetic fields, ~rom a ground and spaGe weather data source 12, sUch as one or more monitors upstre~m and in front of the Eart~ relative to the solar wind, including the NASA ~IND and/or AcE satellites. Real-21 q4429 time data ~ay be provided by the NOAA or through other available sources. The ground and space weat~er data source 12 may p~ovide ground ~agnetometer data from at least one measuring station in the polar cap, as well as other qround arl~ ~p~ce-Dased electromagnetic data measuring, for example, variations in pxecipitation from the Van Allen radiation b¢lts, ~ariation~ in magnetic and electric flelds, etc. All ground and space weather data are tagg~d wit~ t~c ti~a Or _ . ~easurement and instrument lo~ation.
~ '' The disclosed geomagnetic fore~acting system 10 also receives a desired time af a forecast from an operator 14 or, alternatively, increments a previous forecast ~ime stored in the memory 22 to a new forecast time using the processor 20. Using such ground and space weather data, the geomagnetic forecasting system 10 generates and outputs a forecast 16 at each time increment andlor at predetermined or specified tLmes.
In an illustrative embodiment, the geomagnetic forecasting system 10 receives the ground and space weather data at an input device 18, which transmits the ~ound and space ~eather data directly, or pre-processes the ground and space weather data using a data translator and processor 19, . in a manner known in the art, to provide the ground and 21 ~442~
space weathex data in a format for use by a processor 20.
~om~nds ~or the proCessor 20 may also be generated in and trans~itted from the input device 18.
The processor 20 is also connected to a ~emory 22 for s~oring data and stored programs 24. ~he processor 20 proc~sses the ~m~nds and ground and space weather data to yerle~ at~ b-~pUt ua~a signals wnicn ar~ tr~n~mi~t~d to an output dev~ ce 26 for output a6 the g~omagnetic f~ecast 16 which i~ a time-varying response to the i~put ground and 10 space weather data. The resulting forecast 16 may include tL~e-variable patte~ns of cur~ents, electric fields, and Joule heating which may then be used by the processor 20 or other systems ~o calculate geomagnetically induced currents ~ ) and/or associated induced voltages and parameters applicable to communication, navigation, and other a~eas in which geo~agnetic ef~ects cause proble~s. ~or example, the disclosed geomagnetic forecasting system 10 may operate to generate predictions of ionospheric storms which ~ay adversely af~ect electrical power grids.
In an ill~strative~e~bodi~ent, the geomagnetic forecasting system 10 is a "SP~RC 10l' workstation avai~able from SUN MICROSYSTE~5, INC. ~aving a ~iCroprocessor ~s processor 20, about 8 MB associated RAM memory and a hard or . _ -- 2194~29 fixed dri~e as memory 22. T~e processor 20 operates using th~ r~IIX oper~ting sys~em io run application soft~are as the stored proyL c 24, providing pr~grams and subroutines ~or implementing the geo~agnetic forecasting system lO and ~ethod.
T~e input device l~ may lnclude a ~eyboard, ~
~ouse, andJor a data reading device such as a di~ dr~ve ~or rece~ving commands and the ground and space weather da~a in input data files from storage media such as a floppy dis~ or a~ 8 mm storage tape. AlternatiVely, t~e input device 18 ~ay include connections to external sys~ems as the ground and space weather data SoUrce 12 for p~oviding time-tagged real-time ground and space weather data. The received ground and space weather data coming from various locations 15 may be propagated to a conunon input location, and may be averaged appropriately in the data translator and processo~
1~ for inputs to various subsystems.
The received ~round and space weather data may then be stored in memory 22 for further processing to ge~erate the geomagnetic ~orecast 16. In addition, through ~ the input device 18~ the user may select and/or inp~t'.
co~r~n~ uSing a mouse. The input device 18 and outpUt 2 1 9 4 4 2 ~
de~ice 26 may also be incorporated as an input/output (I/O) interface, which may include a graphic user interface tGUI~.
The processor 20 generates t~e output data signals as geomagnetic parameters representi~g the geomagnetic s ~orccast 16, and ~hn OUtpU~ d~t~ slgnal- are ~~nt ~ th0 output device 26 such 25 a display for display thereof.
Alternativel~, the output device 26 may include ~pecialized graphics programs to conve~t ~he generated geomagnetic data to a displayed gr~phic. In additional embodiments, tne outputs may be listed in f ile5 which may ~e elê~.ro~ically _ trans~itted to custome~s or used ~or output as columns or tables of text ~y the output device 26 which may be a display or a ~ard copy printer.
The geomagnetic forecasting syste~n 10 per~orras the application programs and su~routines, described hereinbelow in conjunction ~ith FIGS. 2-6, which aXe implemented from compiled or interpreted source code in t~e FORTRAN
programming language. It is understood that o~e skilled in the a~t would be able to use other programming languages 29 s~ch as C++ to implement ths geomagnetic forecasting system 10 and method. In an illustrative embodiment, the processor 20 of the geomagnetic forecasting system 10 includes a latitude determination generator 28, a plurality of ' -~ , ' I . .
~ ,. .. .
- 2~ 94429 p~edlctlo~ generator~ 30, a conductlvi~y generator 32, an AL
and A~ prediction generator 34, an electric field pattern generator 36, and an adaptive feedback generator 38. Such components 28-38 may be implemented in hardware and/or software for operating in a manner as described below.
As descri~ed herein, the pa~a~eters "AL" and IlAU'' desiqnate indices represen~ing a maximum negative ~ distur~ance and a ~-~Yi- - positive disturbance, respectively, of auroral elect~ojet currents, aq ~nown in o t~e art. AL and AU are component~ of the commonly used AE
index for -~pecifying levels of magnetic activity, in which AE represents the strength of auroral activity on a linear scale. Kp is an index of global magnetic activity on a logarithmic scale.
For clarity of explanation, the illustrative embo~irsnts of the disclosed geomagnetic forecastinq system - 10 ana me~noq are presented as having individual functional blocks, which may include functional blocks labelled as _ . "generator", "processor" and "processing unit". The functions represented by these blocks may be p~ovided through the use of eithe~ shared o~ dedicated hardwa~, lncluding, but not limited to, hardware capahle of executing software. ~or example, the functions of the qenerato~s, processor and processing unit presented herein may be provided ~y a s~ared processor or by a plurality of individual processors. Moreover, the use of t~e functional blocks with accompanying labels herein is not to be construed to refer exclusively ~o ha~dware capable o~
executing so~tware. Illustrative embo~;~qnts may include digita; signal processor ~DSP~ nardwarel such as the AT~
DSP16 or DSP~2C, read-only memory (ROM) ~or storing sof~ware performing the operations discussed ~elow, and random access 0 memory (RAM) for storing DSP results. very large scale integration (VISI) ~ardware embodiments, as well as custo~
VLSI circuitry in combination with a general purpose DSP
circuit, may also be provided. Any and all of these em~odiments may be deemed to fall within the mea~ing o~ the labels for the functional blocks as used ~ere~n.
The l~titude determination generator 28 is responsive to input dates and times for determining the latitude of the sub-solar point, in a manner k~own in the art, such as the methods described in "Explanatory Supplement to the Astronomic~l Al~n~c" (P.K. Seidelmann, ~d.), University Science Books: ~ill Valley, Virginia, 1992; as well as other astronomy textbooks. ~he qeomagnetic forecasting system 10 and method employs a subroutine using, -.
for example, components of the CBAMP code described in J.P.
Kennealy et al., "CBs~; The Celestial Background Scene - Descripto~", P1-TR-9~-2215, Phi~lips La~oratory, Air Force Nateriel Command, Hanscom Air Force ~ase, Massachusetts, January 1993, pp. i.-xii and pp. 1-214. The sub-solar lat_tule determines a portion of the Earth whic~ is illumin~ted, that set~ tn~ eA~er.t G. infiuenee of ~ne solar-- W -induced conductivity in the conductlvity ge~erato~ 32.
~he prediction generators 30 include neural networks and/or similar n~merical or analytlc predictors.
~or ~xample, the prediction generators 30 include a first neural network for determining the equatorward ed~e of the auroral oval at ~idnight; i.e. the MEB. The prediction generators 30 also include a second neural network for dQt~ ining ~he cross-polar-cap potential (PCP). Such neural networks may be trained~and implemented as described in ~. Freeman et al., "The Use of Neural Net~orks to Predict Magnetospheric Parameters for Inputs to a ~agnetospheric Forecast Model", PROCEEDINGS O~ THE INTERNATION~L WORKSHOP
ON ARTIFICIAL INTELLIGENCE APPLICATIONS IN SOLAR-~ERRESTRIAL
PHYSICS (J. Joselyn e~ al., Ed.), Lund~ Sweden, September 22-24, 199~, pp. 167-181. A third neural network may also be used to determine the Kp value and/or a pseudo Kp value.
In one G~ho~i~ent, t~e neural netwo~ks may be trained to provide ~especti~e predictions one hour in advance o~ t~e current coordination time of the geomagnetic forecasting system lO and method; i.e a common temporal reference of the processing of the various components and steps of the geomagnetic ~orecasting system 10 and metho~.
It is understood that prediction generators other than neural networks ~ay be used, such as computer-based ~odels using statistics.
T~e conductivity generator 32 implements a statistically based conductivity value generation method using a conductivity ~odel, such as the technique described in D.A. ~ardy et al., "Stati~tical and Functional Representations of the Pat~erns of Auroral Energy ~lux, Number ~lux, and Conductivity", JOuRNAL OF GEO~YSICAL
- R~S~ARCH, VOL. 92, No. All, November 1, 1987, pp. 12,275-12, 294 .
The AL and AU prediction generator 34 may i~plement an input-state space technigue for predic~ing the 2~ AL and AU values; for example, generator 34 may use the _ techniques deccribed in D. ~7assiliadis et ~1., "~n Emp~r Model Rel~ting the Auroral Geomagnetic Activity to the ~nterplanetary Magnetic Field", GEOPHYSICAL RESEARCH
, : LETTER5, YOL. 20, 1993, pp. 1731-1734; D. Vassiliadis, "The Input-State s~ace App~oach to t~e Prediction of Auroral Geomagne~ic Acti~ity from solar Wind Va~iables", PROCEEDINGS
OF THE INTERNATIO~AL WOR~SHOP ON ARTIF~CIAL I~TELLIGENCE
5 AEP~l~Ail~IONS IN SOI~R ~ER~ESTRIAL PHYSICS (J. ~oselyn et al., Ed.), Lund, Sweden, September 22-24, 1993, pp. 145-151;
and D. ~assiliadis et al., "A Description of Solar Wind-- Magnetosphere Coupling ~a~ed on Nonline~ Flltor~, JouR~A~
OF GEQPE~YSICAL RESEARC~I, VOL. 100, NO. A3 ~ Ma~ch 1, 1995, 10 pp. 3~g5-3512.
The input-state space techni~ues are used to predict the AL and AU indices whiGh are related to the maximum currents in the high latitude ionosphere, as described in grea~er detail below.
In an ill1lstrative embodiment, t~le electri~ ~ield pattern generator 36 Lmplements a statistically based electric field pattern generation technique using an elect~ic ~ield model for determining and adjusting the electric field patterns in a manner known in the art, such as the techniques described in ~ P. Heppner and N.C.
Maynard, "Empirical High-Latitude Electric Field Models~, JOURNAL OF GEOPH~SICAL RESEARCH, V~L. 92, ~O. A5, May l, 1987, pp. 4467-4489; and in F.J. Rich and N.C. ~aynard, 21 q442~
"Consequences of Using Simple Analytical Functions for the ~igh-Latitude Convection Electric Field", JOURNA~ OF
- GEOPHYSICAL RF~RrH~ VOL. 94, NO. A4, April l, 1989, pp.
3687-3701. It is to be understood that other statistical models may be used. For exa~ple, the te~hn i ques described in D.R. Weimer, "Models of High-Latitude Potentials Derived with a Least Error Fit of Spherical ~ar~on~c ~oertlclont~
JO~RNAL OF GEOPHYSICAL RESEARCH, VOL. loo, NO. A10, October 1, 1995, pp. 19,595-19,607; may also be used.
The adaptive ~eedback generator 38 provides a feedback ~echanism for adaptively adjusting electrodynamic and geomagnetic parameters s~ch as electric fields, Joule heating, perpendicular currents, parallel currents, and Hall currents based on u~uLs of the prediction gene~ators 30 and the AL and AU prediction generator 28. ~ing a pa~tern of values o~ one of suc~ geomagnetic parameters, the adaptive feedback generator 38 may determine a characteristic ~ um value of the patte~n Using ~ comparators or a sorting ~echanism.
The ~daptive fee~b~c~ gener~tor 38 rQcpondC _o geo~gnetic indices derived from measurements of the geom~qn~tic parameter at ground level or predicted by other means described herein with reference to the maximum value 660-2 21 9442~
for determining variatio~s in the measurements. Comparator~
or other circuitry may be included in the adapti~e feedback - generator 38 for compari~g the ~ariations with a predicted value of the AL parameter, and then adjusting the geomagnetic pa~meter in resp~nse to the co~pariso~ for scaling the geomagnctic paramet~rE~ wlth ~rer"nO~ to th-~predicted AL and AU parameters.
The processor 20 ~ay include other components ~ j~r ~ortware for perrorming other runc~ions. For example, the processor 20 processes the PC index from re~l-time ground magnetometer data from at least one measu~ing station in the polar cap, using the technique described in O.A. Troshichev et al et al., ~Magnetic Activity in the Polar Cap - A ~ew Index," PLANE~A~Y SPACE SCIENCE, VOL. 36, N0. 11, 1ss8, pp. 1~95-1102. The Pc index may be used a~ an - - - input to the AL and AU prediction generator to aid in initializatio~ a~d to determine real-time AL and AU values to pexform quality control of the predictive values.
As shown in FIG. 2, the met~od includes the steps of starting the generation of forecasts of geomagnetic parameters in step 4~ using the geomaqnetic foxecasting system 10; receiving ground and space weather data in step 42 from data source 12; determining solar-W -induced 21 9442't 66n-2 conductivity in step 44; proces~ing the ~round and space Weather data in step 46; determining intermediate parameters ~rom the processed g~ound and space weather data in step 48;
arlapti~ely det~rrining geomagnet-ic parameters in step 50 associated wit~ the input ground and ~p~CQ we~t~er data ~nd time data; and outputting the geomagnetiC parameter~ in step 52 as the geomagnetic ~orecast 16.
In one em~odiment, the geomagnetic parameters may ~e output on a display as t~e output device 26 in the form of geomagnetic coordinate current patterns, geographic coordinate current patterns, and specific location tracking of the overhead currents and associated gro~nd magnetic variations.
In operation, the method responds to incoming weather and time data, in which upstream solar wind data ~rom the ground and space weathe~ data source 12 is propagated to the nose of the magnetopause with a duration of t~. For each ti~e step of, ~or example, about 2.S
minutes, propagatsd solar ~ind data are applied to the AL
an.~ .at7 preAi~icn gener2tor 3~. fcr prediction of a current v~lue o~ A~ at a Goor~n2tion time nf ~. The c~ren~' v~lue of AL and a set of previous values of A1 are used to p~edict the neXt value of AL at ",, ,,1 '~_ ~' (Ulr + 2 . 5 minutes) .
~ ~ .
The data translator and processor l9 also Lmplements an ~e~ager for averaging the solar wind d~ta using a one hour ~liding boxcar or window te~tgue, ~lth the time assignment for prediction being po6itioned at tne ~iddle of the temporal boxcar The one hour averages are used by the p~ediction gener~tors 30 and the AL and AU
prediction generator 34 as the solar wind data. In one embodiment, the ~olar wind data is averaged at coordination 10 ti~es (UT - X), (U~ - X - 2 . 5 minutes), and (UT - X - 5 minutes) to be input to the prediction generators 30, in which X is t~e lead time for o~taining ~
prediction at time ~UT ~ 2.5 minutes) . For neural networks as prediction gene~ators 30 trained to m~ke predictions one hour in advance, X = one hour.
Fo~ dete~in;ng the electric field patterns, the processor 20 ~ses the value of t~e IMF at time (~T - 25 minu~es) for producing a forecast at ti~e (UT + Z.5 minutes). The ac~ual forecast lead time is t~ = (tp + 2.5 minutes), with the forecasting repeated for each 2.5 minute time step. The geomagnetic forecasting - system 10 ~nd method generates a step-wise continuousl~ -varying pattern with a 2.5 minute resolution.
_ ,~ ~ ''~"
21 q4429 As shown in FIG. 3, the method performs step 44 by starting the determination of solar- W -induced conductivity in step 54; recei~ing times and dates, from the input device 18 in step 56; determining the latitude of a s~b-solar point ~ 5 in step 5B; ~eceiving an F10.7 pa~ameter, as described above, in step 60; and determining the solar ultraviolet ~W3 induced conducti~ity from the latitude and t~e F10.7 para~eter i~ step 62, in a manner as described in F.J. ~ich and N C. Maynard, "Consequences of Using Simple Analytical ~unctions for the High-Latitude Convectio~ Electric ~ie~d", supra.
As shown in FIG. 4, the method performs step 46 by sta~ting t~e processing the ground and space weather data in 6~ep 64; applying the average solar wind velocity, as described above, and interplanetary magnetic field (IMF) data to first and second neural networks in step 66;
~~~ ~ determining the MEB of the auroral zone and t~e PCP using the first an~ seccnd neural networks, respectively, in s~ep 68; determining an electrie field pattern type using t~e IMF
in step 70; and generating predicted ~alues of t~e AL and AU
pa~ameters from t~e solar wind velocity and IMF data in step 72. Step 70 may be a discrete step or may be an integral part of the electric field pattern generator 36.
21 q4429 660-2 -..
~ sing an input-state space approach, the magnetosp~ere is considered to remain on one or anothe~
definite trajectories in phase space that recurrently vi~it a ~elatively small state spa~e region, as opposed to varying ~hroughout the entire state space~available, At any given ~ ti~, '~,e solar wind ~nd geo~agnetic conditions may be encoded in state vectors which express the state of magnetospheric activity. A combina~ion of input ~ariables and output responses may be used to determine the state of 10 the magnetosphere. U~;ing both linear filtering and non-linear dynamics, non-linear fil~ers may be used to describe - the geomagnetic response to the solar wind.
The s~ate vector may be defined from ground magnetometer measure~ents of the ionospheric currents, such lS as the AL index, according to:
- Xfi = ~xnrx~ xn-z~ xn~
where x~ is the value of the index at tilne n.
The input vector is defined from the solar wind as:
,.
. . . -20-~? ~ 21~442~
~60-2 Un = (u~,unl,u~z,-,un~ 2) ~here u~ i5 the value of the input driver; i.e. data, at . _ .
time ~. An input driving function such as a lea~y rectified electric field function may ~e defined as:
u = v(~2 ~ By)~/2sin~(~/2~ ~here tan(~) - s3/~r ~3) with Bz and By being component~ of the IMF, and ~ ~eing the solar wind velocit~. The input-~tate cpacQ i~ th~ ~p~ce (Xnr Un) -The r~ex~ x may ~e determined by:
Xn~l = A-Xn 1- ~-U~ ~4) with A and B being time-varying ~ectors, and ~heir 10 dimensions 1 and m are free parameters which correspond to the memory of the input-state space system to internal and external changes. For a filter determined by (A, B), the input-state space is populated with long, co~tinuous time series from a database, stored in ~emory 22 From the recurrences o~ magnetospheric activity in the input-state space (X~, U~), which may be detected by pattern recognition techniques such as trained neural networks, ~ highly variable magnetosphe~ic response to the ~ 21 9442q solar wind input may be determined through the ~ilter (A, B). The coefficients ~ay be calculated fro~ nearest neighbo~s of a reference point, and the filter coefficients : may then be convolved to predict a next point. once a phace 5 . 6pace trajectory is determined, one input variable ~ay be u~ed to track the behavior as long as it r~i n~ on that tra j ec~ory.
- As shown in FIG. 5, the method per~orms step 48 by starting the generation of intermediate paramete~s in step 74 from the processed weather and time data; and generating the pseudo Xp jn~icator from the MEB in step 76, in a manner as rollows.
In M.S. Gussenhoven-et al ., 1l Systematics of the Equatorward Diffuse Auroral Boundary", JOURNAL OF
15GEOPH~SICAL RESEARC~, VOL. 88, NO. A7, July 1, 1983, pp.
5692-5708, it was determined that the equatorward boundary of the aurora is statistically rel~ted to the Kp index. By i~verting the equations described in M.S. Gussenhoven et al., su~ra, for the dependence of ~he boundary at midnight (MEB) with Kp, one may obtain a relationship for Kp based on the MEB. The first prediction generator uses, for example, a ne~ral network to ~enerate a prediction of the ~EB, and then the geomagnetic system 10 and method using the . .. . ..
relationship of Kp based on the predicted MEB to predict a ~pseudo Kp"; i.e. a parameter related to the Rp index.
. The generation of t~e pseudo Kp produces an index having some of the propexties of the Rp index, but the pseudo Kp changes with a time step of, for example, ahout 2 . 5 minutes, instead of changing at the usually deflned 3 hour ~ime interval o~ Kp; ~e~ce, the ~pseudo ~pll par~meter - is related to 'out not identical with the Kp index known in the art.
o In one embodi~ent, the pseudo Kp indioato~ is updated every 2.5 minutes to provide rapid response scaling of potential and conductivity patterns. In alternative embod1ments, the pseudo Kp indicator may be updated wi~h other ti~e steps. Alternatively, t~e Kp value and/or the pseudo ~p value may be separately determined by a neural network.
The method then continues by determining particle cond~ctivity parameters using the pseudo Kp indicator in step 78; and generating a PCP scaling ratio in step 8~. The PCP of the electric potential pattern generated by the electric ~ield pattern ~en~rator 3 6 is determined from the potential m~ and ~ini ~r of the pattern. The ratio o~
the PCP forecast by the second ne~ral network to the pattern 2 1 9 4 4 2 q PCP determines the PCP scaling ratio used to adjust the electric field pattern.
~ he method then continues by determining Hall and Pedersen conducti~ity by combining the solar-UV-induced conductivity and the particle conductivity in step 82; a~d deter~ining an adjusted electric field pattern ~rom th~
eiectr~ic field pattern and the ~CP ratio in step 84.
T~e static statistical-electric field patterns generated by the electric field pattern generator 3~ are transformed into ti~e-varying patterns by using the PcP
rati~ and the pseudo Xp indicator generated from the prediction generators 30 to adjust the size and scale o~ the electric field patterns. The conductivity patterns determi~ed by the conductivity generator ~2 are also sized using the pseudo ~p indicator from the first neural network of t~e prediction generators 30.
As shown in FIG. 6, the method performs step 50 by 5tarting the adaptive determination of geomagnetic parameters, including Joule heating, total perpendicular currents, parallel currents, ~nd Hall currents in step 86, - as well as other electrodynamic parameters useful in determining geomagnetic phenomena. The method continues by generati~g initial values of the Joule heating, ~ ~ 2 1 94429 perp~icular currents, parallel currents, and Hall currents from the intermediate parameters in step 88; deter ining a ~aximum ~alue of each half o~ the pattern of Hall cu~rents or of the total perpendicular currents in step 90;
determining the variation of the geomagnetic field at ground level with reference to the ~aximum value in step 92;
comparing the variation with a predicted value of the A~ an~
AU parameter in step 94; and a~justing the geomagnetic parameter patterns in response to the compariso~ in step 96.
The geomagnetic forecasting system 10 and method is thus capable of performing time-varying adjustment of static, statistically-based electric ~ield and conductivity patterns using solar-wind-driven neural network predictors and~i~pu~-state space AE, AL, and AU index predictions to provide time-dependent predictions of ionospheric currents, such as Hall, Pedersen, and field-aligned currents; electric fields; and Joule heating in the high-altitude ionosphere for ~pace weather ~orecasting.
Through the use o~ the prediction generators 30, predictions may be obtained for fo~ecasting a high-resolution, pseudo Kp indicator for pattern adjustments, in addition tô forecasting critical boundaries ~nd parameter levels.
:
~ .J 2 1 q 4 4 2 q In one embo~ir~nt, the geomagnetic forecasting ~ystem 10 and method are capable o~ predicting actual ground magnetic field variations created by t~e ionospheric currents with a lead time of about 45 minuteS. Forecast products ba~ed on these current predictions are t~ilored to specification applications. For example, the p~edictions of the geomagnetic forecasting system 10 and method may be used in calculations of local geomagnetically induced currents in power lines tailored to each specific power company. T~ese 1~ predictions allow adjust~ent of loads and sources within each company's power grid, commensurate with the risk of problems for that particular geographic area. Such improved performance and adaptability of, for example, power utilities to respond to geomagnetic disturbances in a preventive r~nPr provide for increased cost benefits.
For a given application, the stored programs 24 may i~lude specific software which the processor 20 executes to perform the application. ~lternatively, the system 10 may include additional processors and~or stored programs (not shown in FIG. 1) for performing the applicatlon. For example, the system 10 and met~od may forecast geomagnetically induced currents from the ground and space weather data. The processor 20 or other ~ r 2 ~ 9 ll 4 2 (~
660~
proce~sors of ~he system 10 may then determine such forecasted geomagnetically induced currents as being hazardous to electric power line operation according to p~edetermined power hazard criteria which is provided to the s~stem 10 in a manner known in the art.
In other embodiments, the processor 20 or ot~er processors of the system 10 may determine such forecasted geomagnetically induced currents as being cond~cive to corrosive deterioration of pipelines accordin~ to p~edetermined current corrosion criteria; as being hazardous to long line communication circuit operation according to predet~r~ined cor~nication hazard c~iteria; as producing harmonics in power lines according to predetermined ~armonics generation criteria; or as producing harmonics in high reliability manu~acturing processes according to predetermined harmonics generation criteria. For example, manu~acturing processes may have six sigma reliability, as known in the art, with such high reliability being ad~ersely affected by the harmonics caused by geomagnetic i~egularities.
In further e~bodiments, the processor 20 or other processors of t~e system 10 may forecast regions of ionospheric irreg~larities and changes from the ground and Z l ~ 4 4 2 9 space weather data, and then detcrmine such regions as indicating degraded radio communications accordinq to - predete~mined radio communications criteria; or as indicating deg~aded accuracy of navigation systems according to p~edeter~ined navigation criteria.
The predetermined criteria ~ay be p~e-programmed or incorporated into the stored programs executed by ~he - processor 20, and thus are ~sed in conjunction with the forecasts by the geomagnetic forecasting system and ~ethod to determine an application-specific analysis of such geomagnetic effects. with such fo~ecasting and analysi~ of the geo~agnetic effects, improved mitigation of t~e geomagnetic effects is attainable.
While the disclosed geomagnetic forecastin~ system and me~hod have been p~rticularly shown and described with reference to the preferred embodi~ents, it is understood by those skilled in the art that various modifications in form and detail may be made therein witho~t departing from the scope and spirit o~ the invention. Accordingly, modifications such as those suggested above, ~t not limited thereto, are to be considered within the ~cope of the invention.
visual mani~estation of these effects is the aurora borealis or northern lights. 5uch geomagnetic activity has been known to disrupt communications, degrade navigation sensors, induce currents in long po~er lines and pipelines, etc.
For example, one magnetic storm is known to have induced sufficient currents to disrup~ the entire ~ydro Quebec power ~rid, caus~ng a four hour blackout of all Of Que~ec. The cause of these disturbances is episodic energy and mass releases f~om the Sun. The prediction of the occurren~e of magnetic storms and substorms and ~heir , .
J
.. ~...~
. - :
21 q442q .
660-2.
effects on ionospheric currents may facilitate - implement~tion of mitigating actions to minimize the adverse effects o~ such storms.
Xnown methods ~or ~orec~tlng U9~ g~o~agnetla S indice~ 6~ch as Kp, AE, An, and AL. Geomagnetlc lnd~es ~re derived ~rom qround magnetometer measurements. Variable currents in the ionosphere cause changes in the Ea~th~s ~agnetic field. The AE, AU, and AL are typically instantaneous quantities determined for each ~inute after the fact, as opposed to forecasts. Also, t~e Kp indicator is typically a three hour index, but not a rapidly varying quantity. In a~dition, the AL and AU indices relate to magnitudes of ionospheric currents at auroral latitudes but may not p~ovide information as to the longit~ n~ l or local distri~tion of such currents or the size o~ patterns of currentS. For t~ese reasons, known forecasting based on t~e Xp, AE, ~, and AL indices are t~us limited in accuracy and do not provide location-specific information on the ionosp~eric currents.
One additional index is the daily average 10.? ~.
~lux level of radio emissions fro~ ~he Sun, labelled ~10.?.
: The F10.7 index is commonly used in the art as a proxy for t~e solar ultraviolet (W) ra~iation that is an ionization source for the iono~phere. This ifi determined daily at ~he Ottawa Observatory ~nd is distributed by the NOAA Space Environment Services ~enter (s~sc).
SU~ARY
~he disclosed geomagnetic forecasting system and method provide the capacity for forecasting geomagnetic parameters and events such as the occurrence of magnetic s~orms and substorms and their e~fects on ionospheric currents using prPdictive and tLme-Varyi~g ionospheric and space-based measurements uith improved information on t~e dist~ibution, size, a~d specific location of current pa~terns. ~Such a sys~em and met~od facilitate _. implementation of mitigating actions to rinimi~e t~e effects of fiuch stcrms.
A system and method f or forecasting geomagnetic events from ground and space weather ~a~a, including solar wind velocity data and interplane~a~y magnetic field da~a are disclosed, having a processor including a first prediction generator for predic~ing a midnig~t equatorward ~oundary (M~B) of the diffuse au~ora from the ground and space weather data; a second p~ediction generator for predicting a polar cap potential tPCP) value from the ground ~nd space ~ea~he~ data; an Al and AU prediction generator for predicting AL and A~ val~es from the ground ~nd space weat~er data; ~eans ~or gen~ating a Kp value from the MEB
value; an e~ectric field pattern gene~ator for determining electric field patterns from the Kp value, the PCP v~lue and 5 the ground and space weather data; a conductlvity generator G~ uetermining conductivity values from the fo~ecas~ time, the gr~ d -r,d ~pac~ ~ca~he~ d~ta, and the Kp value, and an adap~ive ~eedback generator for adaptively generating the geomagnetîc pa~ameters from the conductivity v~lues, the IO ele~tric field ~alues, and the predicted ~L and AU values.
BRIEF DESCRI~TTON OP ~E DRAWINGS
The features of the disclosed geomagnetic ~orecasting system and method will become more readily a~parent and may be ~e~ter understood by referring to the Iollowing detailed description of lllustrative embodiments of the presen~ invention, taken in conjunction with the acco~panying d~awings, in whic~:
FIG. 1 is a block diagram of the disclosed geomagnetic forecasting syste~;
FIG. 2 is a flowchart of the method of ope~ation o~ the disclosed geomagnetic forecasting syste~; 1 ~ FIG. ~ is a flowchart for dQtermining solar-W-induce~ con.ductl~iTi'y;
' 219442~
.
~ . ~. FIG. 4 is a flo~chart for the ps~cPs6ing ol input ground and space weathex data;
FI&. 5 is a flowchart for the generation of intermediate pa~meters; and FIG. 6 is a flowchart for the adaptive determination of qeomaqnetic para.meterc a5 o~t~lat ~c~e~s'~.
~SCRIPTION OF THE PREFERRED EMBODIMENTS
Referring now in specific detail to the d~awings, with li~e ~eference numeral6 identifying s~m~lar or identical eleme~ts, as shown in FIG. 1, the present aisciosure deBcribe~ a geomagnetic forecasting system 10 and method for ~orecasting ~eomagnetic parameters and events such as the occurrence of ma~netic storms and substorms and their effects on ionospheric currents using ionospheric and ~pace-based measuXements.
In an illustrative embodiment, the geomagnetic forecasting system 10 receives ground and space ~eather data, including data reflecting activity of the sola~ wind, such as solar ~ind velocity, as well as data measuring 20 interplanet~y magnetic fields, ~rom a ground and spaGe weather data source 12, sUch as one or more monitors upstre~m and in front of the Eart~ relative to the solar wind, including the NASA ~IND and/or AcE satellites. Real-21 q4429 time data ~ay be provided by the NOAA or through other available sources. The ground and space weat~er data source 12 may p~ovide ground ~agnetometer data from at least one measuring station in the polar cap, as well as other qround arl~ ~p~ce-Dased electromagnetic data measuring, for example, variations in pxecipitation from the Van Allen radiation b¢lts, ~ariation~ in magnetic and electric flelds, etc. All ground and space weather data are tagg~d wit~ t~c ti~a Or _ . ~easurement and instrument lo~ation.
~ '' The disclosed geomagnetic fore~acting system 10 also receives a desired time af a forecast from an operator 14 or, alternatively, increments a previous forecast ~ime stored in the memory 22 to a new forecast time using the processor 20. Using such ground and space weather data, the geomagnetic forecasting system 10 generates and outputs a forecast 16 at each time increment andlor at predetermined or specified tLmes.
In an illustrative embodiment, the geomagnetic forecasting system 10 receives the ground and space weather data at an input device 18, which transmits the ~ound and space ~eather data directly, or pre-processes the ground and space weather data using a data translator and processor 19, . in a manner known in the art, to provide the ground and 21 ~442~
space weathex data in a format for use by a processor 20.
~om~nds ~or the proCessor 20 may also be generated in and trans~itted from the input device 18.
The processor 20 is also connected to a ~emory 22 for s~oring data and stored programs 24. ~he processor 20 proc~sses the ~m~nds and ground and space weather data to yerle~ at~ b-~pUt ua~a signals wnicn ar~ tr~n~mi~t~d to an output dev~ ce 26 for output a6 the g~omagnetic f~ecast 16 which i~ a time-varying response to the i~put ground and 10 space weather data. The resulting forecast 16 may include tL~e-variable patte~ns of cur~ents, electric fields, and Joule heating which may then be used by the processor 20 or other systems ~o calculate geomagnetically induced currents ~ ) and/or associated induced voltages and parameters applicable to communication, navigation, and other a~eas in which geo~agnetic ef~ects cause proble~s. ~or example, the disclosed geomagnetic forecasting system 10 may operate to generate predictions of ionospheric storms which ~ay adversely af~ect electrical power grids.
In an ill~strative~e~bodi~ent, the geomagnetic forecasting system 10 is a "SP~RC 10l' workstation avai~able from SUN MICROSYSTE~5, INC. ~aving a ~iCroprocessor ~s processor 20, about 8 MB associated RAM memory and a hard or . _ -- 2194~29 fixed dri~e as memory 22. T~e processor 20 operates using th~ r~IIX oper~ting sys~em io run application soft~are as the stored proyL c 24, providing pr~grams and subroutines ~or implementing the geo~agnetic forecasting system lO and ~ethod.
T~e input device l~ may lnclude a ~eyboard, ~
~ouse, andJor a data reading device such as a di~ dr~ve ~or rece~ving commands and the ground and space weather da~a in input data files from storage media such as a floppy dis~ or a~ 8 mm storage tape. AlternatiVely, t~e input device 18 ~ay include connections to external sys~ems as the ground and space weather data SoUrce 12 for p~oviding time-tagged real-time ground and space weather data. The received ground and space weather data coming from various locations 15 may be propagated to a conunon input location, and may be averaged appropriately in the data translator and processo~
1~ for inputs to various subsystems.
The received ~round and space weather data may then be stored in memory 22 for further processing to ge~erate the geomagnetic ~orecast 16. In addition, through ~ the input device 18~ the user may select and/or inp~t'.
co~r~n~ uSing a mouse. The input device 18 and outpUt 2 1 9 4 4 2 ~
de~ice 26 may also be incorporated as an input/output (I/O) interface, which may include a graphic user interface tGUI~.
The processor 20 generates t~e output data signals as geomagnetic parameters representi~g the geomagnetic s ~orccast 16, and ~hn OUtpU~ d~t~ slgnal- are ~~nt ~ th0 output device 26 such 25 a display for display thereof.
Alternativel~, the output device 26 may include ~pecialized graphics programs to conve~t ~he generated geomagnetic data to a displayed gr~phic. In additional embodiments, tne outputs may be listed in f ile5 which may ~e elê~.ro~ically _ trans~itted to custome~s or used ~or output as columns or tables of text ~y the output device 26 which may be a display or a ~ard copy printer.
The geomagnetic forecasting syste~n 10 per~orras the application programs and su~routines, described hereinbelow in conjunction ~ith FIGS. 2-6, which aXe implemented from compiled or interpreted source code in t~e FORTRAN
programming language. It is understood that o~e skilled in the a~t would be able to use other programming languages 29 s~ch as C++ to implement ths geomagnetic forecasting system 10 and method. In an illustrative embodiment, the processor 20 of the geomagnetic forecasting system 10 includes a latitude determination generator 28, a plurality of ' -~ , ' I . .
~ ,. .. .
- 2~ 94429 p~edlctlo~ generator~ 30, a conductlvi~y generator 32, an AL
and A~ prediction generator 34, an electric field pattern generator 36, and an adaptive feedback generator 38. Such components 28-38 may be implemented in hardware and/or software for operating in a manner as described below.
As descri~ed herein, the pa~a~eters "AL" and IlAU'' desiqnate indices represen~ing a maximum negative ~ distur~ance and a ~-~Yi- - positive disturbance, respectively, of auroral elect~ojet currents, aq ~nown in o t~e art. AL and AU are component~ of the commonly used AE
index for -~pecifying levels of magnetic activity, in which AE represents the strength of auroral activity on a linear scale. Kp is an index of global magnetic activity on a logarithmic scale.
For clarity of explanation, the illustrative embo~irsnts of the disclosed geomagnetic forecastinq system - 10 ana me~noq are presented as having individual functional blocks, which may include functional blocks labelled as _ . "generator", "processor" and "processing unit". The functions represented by these blocks may be p~ovided through the use of eithe~ shared o~ dedicated hardwa~, lncluding, but not limited to, hardware capahle of executing software. ~or example, the functions of the qenerato~s, processor and processing unit presented herein may be provided ~y a s~ared processor or by a plurality of individual processors. Moreover, the use of t~e functional blocks with accompanying labels herein is not to be construed to refer exclusively ~o ha~dware capable o~
executing so~tware. Illustrative embo~;~qnts may include digita; signal processor ~DSP~ nardwarel such as the AT~
DSP16 or DSP~2C, read-only memory (ROM) ~or storing sof~ware performing the operations discussed ~elow, and random access 0 memory (RAM) for storing DSP results. very large scale integration (VISI) ~ardware embodiments, as well as custo~
VLSI circuitry in combination with a general purpose DSP
circuit, may also be provided. Any and all of these em~odiments may be deemed to fall within the mea~ing o~ the labels for the functional blocks as used ~ere~n.
The l~titude determination generator 28 is responsive to input dates and times for determining the latitude of the sub-solar point, in a manner k~own in the art, such as the methods described in "Explanatory Supplement to the Astronomic~l Al~n~c" (P.K. Seidelmann, ~d.), University Science Books: ~ill Valley, Virginia, 1992; as well as other astronomy textbooks. ~he qeomagnetic forecasting system 10 and method employs a subroutine using, -.
for example, components of the CBAMP code described in J.P.
Kennealy et al., "CBs~; The Celestial Background Scene - Descripto~", P1-TR-9~-2215, Phi~lips La~oratory, Air Force Nateriel Command, Hanscom Air Force ~ase, Massachusetts, January 1993, pp. i.-xii and pp. 1-214. The sub-solar lat_tule determines a portion of the Earth whic~ is illumin~ted, that set~ tn~ eA~er.t G. infiuenee of ~ne solar-- W -induced conductivity in the conductlvity ge~erato~ 32.
~he prediction generators 30 include neural networks and/or similar n~merical or analytlc predictors.
~or ~xample, the prediction generators 30 include a first neural network for determining the equatorward ed~e of the auroral oval at ~idnight; i.e. the MEB. The prediction generators 30 also include a second neural network for dQt~ ining ~he cross-polar-cap potential (PCP). Such neural networks may be trained~and implemented as described in ~. Freeman et al., "The Use of Neural Net~orks to Predict Magnetospheric Parameters for Inputs to a ~agnetospheric Forecast Model", PROCEEDINGS O~ THE INTERNATION~L WORKSHOP
ON ARTIFICIAL INTELLIGENCE APPLICATIONS IN SOLAR-~ERRESTRIAL
PHYSICS (J. Joselyn e~ al., Ed.), Lund~ Sweden, September 22-24, 199~, pp. 167-181. A third neural network may also be used to determine the Kp value and/or a pseudo Kp value.
In one G~ho~i~ent, t~e neural netwo~ks may be trained to provide ~especti~e predictions one hour in advance o~ t~e current coordination time of the geomagnetic forecasting system lO and method; i.e a common temporal reference of the processing of the various components and steps of the geomagnetic ~orecasting system 10 and metho~.
It is understood that prediction generators other than neural networks ~ay be used, such as computer-based ~odels using statistics.
T~e conductivity generator 32 implements a statistically based conductivity value generation method using a conductivity ~odel, such as the technique described in D.A. ~ardy et al., "Stati~tical and Functional Representations of the Pat~erns of Auroral Energy ~lux, Number ~lux, and Conductivity", JOuRNAL OF GEO~YSICAL
- R~S~ARCH, VOL. 92, No. All, November 1, 1987, pp. 12,275-12, 294 .
The AL and AU prediction generator 34 may i~plement an input-state space technigue for predic~ing the 2~ AL and AU values; for example, generator 34 may use the _ techniques deccribed in D. ~7assiliadis et ~1., "~n Emp~r Model Rel~ting the Auroral Geomagnetic Activity to the ~nterplanetary Magnetic Field", GEOPHYSICAL RESEARCH
, : LETTER5, YOL. 20, 1993, pp. 1731-1734; D. Vassiliadis, "The Input-State s~ace App~oach to t~e Prediction of Auroral Geomagne~ic Acti~ity from solar Wind Va~iables", PROCEEDINGS
OF THE INTERNATIO~AL WOR~SHOP ON ARTIF~CIAL I~TELLIGENCE
5 AEP~l~Ail~IONS IN SOI~R ~ER~ESTRIAL PHYSICS (J. ~oselyn et al., Ed.), Lund, Sweden, September 22-24, 1993, pp. 145-151;
and D. ~assiliadis et al., "A Description of Solar Wind-- Magnetosphere Coupling ~a~ed on Nonline~ Flltor~, JouR~A~
OF GEQPE~YSICAL RESEARC~I, VOL. 100, NO. A3 ~ Ma~ch 1, 1995, 10 pp. 3~g5-3512.
The input-state space techni~ues are used to predict the AL and AU indices whiGh are related to the maximum currents in the high latitude ionosphere, as described in grea~er detail below.
In an ill1lstrative embodiment, t~le electri~ ~ield pattern generator 36 Lmplements a statistically based electric field pattern generation technique using an elect~ic ~ield model for determining and adjusting the electric field patterns in a manner known in the art, such as the techniques described in ~ P. Heppner and N.C.
Maynard, "Empirical High-Latitude Electric Field Models~, JOURNAL OF GEOPH~SICAL RESEARCH, V~L. 92, ~O. A5, May l, 1987, pp. 4467-4489; and in F.J. Rich and N.C. ~aynard, 21 q442~
"Consequences of Using Simple Analytical Functions for the ~igh-Latitude Convection Electric Field", JOURNA~ OF
- GEOPHYSICAL RF~RrH~ VOL. 94, NO. A4, April l, 1989, pp.
3687-3701. It is to be understood that other statistical models may be used. For exa~ple, the te~hn i ques described in D.R. Weimer, "Models of High-Latitude Potentials Derived with a Least Error Fit of Spherical ~ar~on~c ~oertlclont~
JO~RNAL OF GEOPHYSICAL RESEARCH, VOL. loo, NO. A10, October 1, 1995, pp. 19,595-19,607; may also be used.
The adaptive ~eedback generator 38 provides a feedback ~echanism for adaptively adjusting electrodynamic and geomagnetic parameters s~ch as electric fields, Joule heating, perpendicular currents, parallel currents, and Hall currents based on u~uLs of the prediction gene~ators 30 and the AL and AU prediction generator 28. ~ing a pa~tern of values o~ one of suc~ geomagnetic parameters, the adaptive feedback generator 38 may determine a characteristic ~ um value of the patte~n Using ~ comparators or a sorting ~echanism.
The ~daptive fee~b~c~ gener~tor 38 rQcpondC _o geo~gnetic indices derived from measurements of the geom~qn~tic parameter at ground level or predicted by other means described herein with reference to the maximum value 660-2 21 9442~
for determining variatio~s in the measurements. Comparator~
or other circuitry may be included in the adapti~e feedback - generator 38 for compari~g the ~ariations with a predicted value of the AL parameter, and then adjusting the geomagnetic pa~meter in resp~nse to the co~pariso~ for scaling the geomagnctic paramet~rE~ wlth ~rer"nO~ to th-~predicted AL and AU parameters.
The processor 20 ~ay include other components ~ j~r ~ortware for perrorming other runc~ions. For example, the processor 20 processes the PC index from re~l-time ground magnetometer data from at least one measu~ing station in the polar cap, using the technique described in O.A. Troshichev et al et al., ~Magnetic Activity in the Polar Cap - A ~ew Index," PLANE~A~Y SPACE SCIENCE, VOL. 36, N0. 11, 1ss8, pp. 1~95-1102. The Pc index may be used a~ an - - - input to the AL and AU prediction generator to aid in initializatio~ a~d to determine real-time AL and AU values to pexform quality control of the predictive values.
As shown in FIG. 2, the met~od includes the steps of starting the generation of forecasts of geomagnetic parameters in step 4~ using the geomaqnetic foxecasting system 10; receiving ground and space weather data in step 42 from data source 12; determining solar-W -induced 21 9442't 66n-2 conductivity in step 44; proces~ing the ~round and space Weather data in step 46; determining intermediate parameters ~rom the processed g~ound and space weather data in step 48;
arlapti~ely det~rrining geomagnet-ic parameters in step 50 associated wit~ the input ground and ~p~CQ we~t~er data ~nd time data; and outputting the geomagnetiC parameter~ in step 52 as the geomagnetic ~orecast 16.
In one em~odiment, the geomagnetic parameters may ~e output on a display as t~e output device 26 in the form of geomagnetic coordinate current patterns, geographic coordinate current patterns, and specific location tracking of the overhead currents and associated gro~nd magnetic variations.
In operation, the method responds to incoming weather and time data, in which upstream solar wind data ~rom the ground and space weathe~ data source 12 is propagated to the nose of the magnetopause with a duration of t~. For each ti~e step of, ~or example, about 2.S
minutes, propagatsd solar ~ind data are applied to the AL
an.~ .at7 preAi~icn gener2tor 3~. fcr prediction of a current v~lue o~ A~ at a Goor~n2tion time nf ~. The c~ren~' v~lue of AL and a set of previous values of A1 are used to p~edict the neXt value of AL at ",, ,,1 '~_ ~' (Ulr + 2 . 5 minutes) .
~ ~ .
The data translator and processor l9 also Lmplements an ~e~ager for averaging the solar wind d~ta using a one hour ~liding boxcar or window te~tgue, ~lth the time assignment for prediction being po6itioned at tne ~iddle of the temporal boxcar The one hour averages are used by the p~ediction gener~tors 30 and the AL and AU
prediction generator 34 as the solar wind data. In one embodiment, the ~olar wind data is averaged at coordination 10 ti~es (UT - X), (U~ - X - 2 . 5 minutes), and (UT - X - 5 minutes) to be input to the prediction generators 30, in which X is t~e lead time for o~taining ~
prediction at time ~UT ~ 2.5 minutes) . For neural networks as prediction gene~ators 30 trained to m~ke predictions one hour in advance, X = one hour.
Fo~ dete~in;ng the electric field patterns, the processor 20 ~ses the value of t~e IMF at time (~T - 25 minu~es) for producing a forecast at ti~e (UT + Z.5 minutes). The ac~ual forecast lead time is t~ = (tp + 2.5 minutes), with the forecasting repeated for each 2.5 minute time step. The geomagnetic forecasting - system 10 ~nd method generates a step-wise continuousl~ -varying pattern with a 2.5 minute resolution.
_ ,~ ~ ''~"
21 q4429 As shown in FIG. 3, the method performs step 44 by starting the determination of solar- W -induced conductivity in step 54; recei~ing times and dates, from the input device 18 in step 56; determining the latitude of a s~b-solar point ~ 5 in step 5B; ~eceiving an F10.7 pa~ameter, as described above, in step 60; and determining the solar ultraviolet ~W3 induced conducti~ity from the latitude and t~e F10.7 para~eter i~ step 62, in a manner as described in F.J. ~ich and N C. Maynard, "Consequences of Using Simple Analytical ~unctions for the High-Latitude Convectio~ Electric ~ie~d", supra.
As shown in FIG. 4, the method performs step 46 by sta~ting t~e processing the ground and space weather data in 6~ep 64; applying the average solar wind velocity, as described above, and interplanetary magnetic field (IMF) data to first and second neural networks in step 66;
~~~ ~ determining the MEB of the auroral zone and t~e PCP using the first an~ seccnd neural networks, respectively, in s~ep 68; determining an electrie field pattern type using t~e IMF
in step 70; and generating predicted ~alues of t~e AL and AU
pa~ameters from t~e solar wind velocity and IMF data in step 72. Step 70 may be a discrete step or may be an integral part of the electric field pattern generator 36.
21 q4429 660-2 -..
~ sing an input-state space approach, the magnetosp~ere is considered to remain on one or anothe~
definite trajectories in phase space that recurrently vi~it a ~elatively small state spa~e region, as opposed to varying ~hroughout the entire state space~available, At any given ~ ti~, '~,e solar wind ~nd geo~agnetic conditions may be encoded in state vectors which express the state of magnetospheric activity. A combina~ion of input ~ariables and output responses may be used to determine the state of 10 the magnetosphere. U~;ing both linear filtering and non-linear dynamics, non-linear fil~ers may be used to describe - the geomagnetic response to the solar wind.
The s~ate vector may be defined from ground magnetometer measure~ents of the ionospheric currents, such lS as the AL index, according to:
- Xfi = ~xnrx~ xn-z~ xn~
where x~ is the value of the index at tilne n.
The input vector is defined from the solar wind as:
,.
. . . -20-~? ~ 21~442~
~60-2 Un = (u~,unl,u~z,-,un~ 2) ~here u~ i5 the value of the input driver; i.e. data, at . _ .
time ~. An input driving function such as a lea~y rectified electric field function may ~e defined as:
u = v(~2 ~ By)~/2sin~(~/2~ ~here tan(~) - s3/~r ~3) with Bz and By being component~ of the IMF, and ~ ~eing the solar wind velocit~. The input-~tate cpacQ i~ th~ ~p~ce (Xnr Un) -The r~ex~ x may ~e determined by:
Xn~l = A-Xn 1- ~-U~ ~4) with A and B being time-varying ~ectors, and ~heir 10 dimensions 1 and m are free parameters which correspond to the memory of the input-state space system to internal and external changes. For a filter determined by (A, B), the input-state space is populated with long, co~tinuous time series from a database, stored in ~emory 22 From the recurrences o~ magnetospheric activity in the input-state space (X~, U~), which may be detected by pattern recognition techniques such as trained neural networks, ~ highly variable magnetosphe~ic response to the ~ 21 9442q solar wind input may be determined through the ~ilter (A, B). The coefficients ~ay be calculated fro~ nearest neighbo~s of a reference point, and the filter coefficients : may then be convolved to predict a next point. once a phace 5 . 6pace trajectory is determined, one input variable ~ay be u~ed to track the behavior as long as it r~i n~ on that tra j ec~ory.
- As shown in FIG. 5, the method per~orms step 48 by starting the generation of intermediate paramete~s in step 74 from the processed weather and time data; and generating the pseudo Xp jn~icator from the MEB in step 76, in a manner as rollows.
In M.S. Gussenhoven-et al ., 1l Systematics of the Equatorward Diffuse Auroral Boundary", JOURNAL OF
15GEOPH~SICAL RESEARC~, VOL. 88, NO. A7, July 1, 1983, pp.
5692-5708, it was determined that the equatorward boundary of the aurora is statistically rel~ted to the Kp index. By i~verting the equations described in M.S. Gussenhoven et al., su~ra, for the dependence of ~he boundary at midnight (MEB) with Kp, one may obtain a relationship for Kp based on the MEB. The first prediction generator uses, for example, a ne~ral network to ~enerate a prediction of the ~EB, and then the geomagnetic system 10 and method using the . .. . ..
relationship of Kp based on the predicted MEB to predict a ~pseudo Kp"; i.e. a parameter related to the Rp index.
. The generation of t~e pseudo Kp produces an index having some of the propexties of the Rp index, but the pseudo Kp changes with a time step of, for example, ahout 2 . 5 minutes, instead of changing at the usually deflned 3 hour ~ime interval o~ Kp; ~e~ce, the ~pseudo ~pll par~meter - is related to 'out not identical with the Kp index known in the art.
o In one embodi~ent, the pseudo Kp indioato~ is updated every 2.5 minutes to provide rapid response scaling of potential and conductivity patterns. In alternative embod1ments, the pseudo Kp indicator may be updated wi~h other ti~e steps. Alternatively, t~e Kp value and/or the pseudo ~p value may be separately determined by a neural network.
The method then continues by determining particle cond~ctivity parameters using the pseudo Kp indicator in step 78; and generating a PCP scaling ratio in step 8~. The PCP of the electric potential pattern generated by the electric ~ield pattern ~en~rator 3 6 is determined from the potential m~ and ~ini ~r of the pattern. The ratio o~
the PCP forecast by the second ne~ral network to the pattern 2 1 9 4 4 2 q PCP determines the PCP scaling ratio used to adjust the electric field pattern.
~ he method then continues by determining Hall and Pedersen conducti~ity by combining the solar-UV-induced conductivity and the particle conductivity in step 82; a~d deter~ining an adjusted electric field pattern ~rom th~
eiectr~ic field pattern and the ~CP ratio in step 84.
T~e static statistical-electric field patterns generated by the electric field pattern generator 3~ are transformed into ti~e-varying patterns by using the PcP
rati~ and the pseudo Xp indicator generated from the prediction generators 30 to adjust the size and scale o~ the electric field patterns. The conductivity patterns determi~ed by the conductivity generator ~2 are also sized using the pseudo ~p indicator from the first neural network of t~e prediction generators 30.
As shown in FIG. 6, the method performs step 50 by 5tarting the adaptive determination of geomagnetic parameters, including Joule heating, total perpendicular currents, parallel currents, ~nd Hall currents in step 86, - as well as other electrodynamic parameters useful in determining geomagnetic phenomena. The method continues by generati~g initial values of the Joule heating, ~ ~ 2 1 94429 perp~icular currents, parallel currents, and Hall currents from the intermediate parameters in step 88; deter ining a ~aximum ~alue of each half o~ the pattern of Hall cu~rents or of the total perpendicular currents in step 90;
determining the variation of the geomagnetic field at ground level with reference to the ~aximum value in step 92;
comparing the variation with a predicted value of the A~ an~
AU parameter in step 94; and a~justing the geomagnetic parameter patterns in response to the compariso~ in step 96.
The geomagnetic forecasting system 10 and method is thus capable of performing time-varying adjustment of static, statistically-based electric ~ield and conductivity patterns using solar-wind-driven neural network predictors and~i~pu~-state space AE, AL, and AU index predictions to provide time-dependent predictions of ionospheric currents, such as Hall, Pedersen, and field-aligned currents; electric fields; and Joule heating in the high-altitude ionosphere for ~pace weather ~orecasting.
Through the use o~ the prediction generators 30, predictions may be obtained for fo~ecasting a high-resolution, pseudo Kp indicator for pattern adjustments, in addition tô forecasting critical boundaries ~nd parameter levels.
:
~ .J 2 1 q 4 4 2 q In one embo~ir~nt, the geomagnetic forecasting ~ystem 10 and method are capable o~ predicting actual ground magnetic field variations created by t~e ionospheric currents with a lead time of about 45 minuteS. Forecast products ba~ed on these current predictions are t~ilored to specification applications. For example, the p~edictions of the geomagnetic forecasting system 10 and method may be used in calculations of local geomagnetically induced currents in power lines tailored to each specific power company. T~ese 1~ predictions allow adjust~ent of loads and sources within each company's power grid, commensurate with the risk of problems for that particular geographic area. Such improved performance and adaptability of, for example, power utilities to respond to geomagnetic disturbances in a preventive r~nPr provide for increased cost benefits.
For a given application, the stored programs 24 may i~lude specific software which the processor 20 executes to perform the application. ~lternatively, the system 10 may include additional processors and~or stored programs (not shown in FIG. 1) for performing the applicatlon. For example, the system 10 and met~od may forecast geomagnetically induced currents from the ground and space weather data. The processor 20 or other ~ r 2 ~ 9 ll 4 2 (~
660~
proce~sors of ~he system 10 may then determine such forecasted geomagnetically induced currents as being hazardous to electric power line operation according to p~edetermined power hazard criteria which is provided to the s~stem 10 in a manner known in the art.
In other embodiments, the processor 20 or ot~er processors of the system 10 may determine such forecasted geomagnetically induced currents as being cond~cive to corrosive deterioration of pipelines accordin~ to p~edetermined current corrosion criteria; as being hazardous to long line communication circuit operation according to predet~r~ined cor~nication hazard c~iteria; as producing harmonics in power lines according to predetermined ~armonics generation criteria; or as producing harmonics in high reliability manu~acturing processes according to predetermined harmonics generation criteria. For example, manu~acturing processes may have six sigma reliability, as known in the art, with such high reliability being ad~ersely affected by the harmonics caused by geomagnetic i~egularities.
In further e~bodiments, the processor 20 or other processors of t~e system 10 may forecast regions of ionospheric irreg~larities and changes from the ground and Z l ~ 4 4 2 9 space weather data, and then detcrmine such regions as indicating degraded radio communications accordinq to - predete~mined radio communications criteria; or as indicating deg~aded accuracy of navigation systems according to p~edeter~ined navigation criteria.
The predetermined criteria ~ay be p~e-programmed or incorporated into the stored programs executed by ~he - processor 20, and thus are ~sed in conjunction with the forecasts by the geomagnetic forecasting system and ~ethod to determine an application-specific analysis of such geomagnetic effects. with such fo~ecasting and analysi~ of the geo~agnetic effects, improved mitigation of t~e geomagnetic effects is attainable.
While the disclosed geomagnetic forecastin~ system and me~hod have been p~rticularly shown and described with reference to the preferred embodi~ents, it is understood by those skilled in the art that various modifications in form and detail may be made therein witho~t departing from the scope and spirit o~ the invention. Accordingly, modifications such as those suggested above, ~t not limited thereto, are to be considered within the ~cope of the invention.
Claims (19)
1. A system for forecasting geomagnetic events and resulting ionospheric currents from ground and space weather data, including solar wind velocity data and interplanetary magnetic field data, the system comprising:
a processor including:
a first prediction generator for predicting a midnight equatorial boundary (MEB) value from the ground and space weather data;
a second prediction generator for predicting a polar cap potential (PCP) value from the ground and space weather data;
an AL and AU prediction generator for predicting AL and AU values from the ground and space weather data;
means for generating a Kp-related value from the MEB value;
an electric field pattern generator for determining electric field patterns from the Kp-related value, the PCP value, and the ground and space weather data;
a conductivity generator for determining conductivity values from the ground and space weather data and the KP-related value; and an adaptive feedback generator for adaptively generating geomagnetic parameters from the conductivity values, the electric field patterns, and the predicted AL and AU values using feedback, the geomagnetic parameters indicating the forecasted geomagnetic events and resulting ionospheric currents.
a processor including:
a first prediction generator for predicting a midnight equatorial boundary (MEB) value from the ground and space weather data;
a second prediction generator for predicting a polar cap potential (PCP) value from the ground and space weather data;
an AL and AU prediction generator for predicting AL and AU values from the ground and space weather data;
means for generating a Kp-related value from the MEB value;
an electric field pattern generator for determining electric field patterns from the Kp-related value, the PCP value, and the ground and space weather data;
a conductivity generator for determining conductivity values from the ground and space weather data and the KP-related value; and an adaptive feedback generator for adaptively generating geomagnetic parameters from the conductivity values, the electric field patterns, and the predicted AL and AU values using feedback, the geomagnetic parameters indicating the forecasted geomagnetic events and resulting ionospheric currents.
2. The system of claim 1 wherein the first prediction generator includes a neural network for predicting the MEB value from the ground and space weather data.
3. The system of claim 1 wherein the second prediction generator includes a neural network for predicting the PCP value from the ground and space weather data.
4. The system of claim 1 wherein the processor forecasts geomagnetically induced currents from the ground and space weather data, and determines such forecasted geomagnetically induced currents as being hazardous to electric power line operation according to predetermined power hazard criteria.
5. The system of claim 1 wherein the processor forecasts geomagnetically induced voltages from the ground and space weather data, and determines such forecasted geomagnetically induced voltages as being hazardous to electric power line operation according to predetermined power hazard criteria.
6. The system of claim 1 wherein the processor forecasts geomagnetically induced currents from the ground and space weather data, and determines such forecasted geomagnetically induced currents as being conducive to corrosive deterioration of pipelines according to predetermined current corrosion criteria.
7. The system of claim 1 wherein the processor forecasts geomagnetically induced voltages from the ground and space weather data, and determines such forecasted geomagnetically induced voltages as being conducive to corrosive deterioration of pipelines according to predetermined current corrosion criteria.
8. The system of claim 1 wherein the processor forecasts geomagnetically induced currents from the ground and space weather data, and determines such forecasted geomagnetically induced currents as being hazardous to long line communication circuit operation according to predetermined communication hazard criteria.
9. The system of claim 1 wherein the processor forecasts geomagnetically induced voltages from the ground and space weather data, and determines such forecasted geomagnetically induced voltages as being hazardous to long line communication circuit operation according to predetermined communication hazard criteria.
10. The system of claim 1 wherein the processor forecasts geomagnetically induced currents from the ground and space weather data, and determines such forecasted geomagnetically induced currents which produce harmonics in power lines according to predetermined harmonics generation criteria.
11. The system of claim 1 wherein the processor forecasts geomagnetically induced currents from the ground and space weather data, and determines such forecasted geomagnetically induced currents which produce harmonics in six sigma manufacturing processes according to predetermined harmonics generation criteria.
12. The system of claim 1 wherein the processor forecasts regions of ionospheric irregularities and changes from the ground and space weather data, and determines such regions as indicating degraded radio communications according to predetermined radio communications criteria.
13. The system of claim 1 wherein the processor forecasts regions of ionospheric irregularities and changes from the ground and space weather data, and determines such regions as indicating degraded accuracy of navigation systems according to predetermined navigation criteria.
14. A system for forecasting time-varying geomagnetic events and resulting time-varying ionospheric currents from ground and space weather data, including solar wind velocity data and interplanetary magnetic field data, the system comprising:
a processor including:
a first prediction generator for predicting a midnight equatorial boundary (MEB) value from the ground and space weather data;
a second prediction generator for predicting a polar cap potential (PCP) value from the ground and space weather data;
an AL and AU prediction generator for predicting AL and AU values from the ground and space weather data;
means for generating a time-varying Kp-related value from the MEB value;
an electric field pattern generator for determining time-varying electric field patterns from the Kp-related value, the PCP value, and the ground and space weather data;
a conductivity generator for determining conductivity values from the ground and space weather data and the time-varying Kp-related value; and an adaptive feedback generator for adaptively generating time-varying geomagnetic parameters from the conductivity values, the time-varying electric field patterns, and the predicted AL and AU values using feedback, the time-varying geomagnetic parameters indicating the forecasted geomagnetic events and resulting ionospheric currents.
a processor including:
a first prediction generator for predicting a midnight equatorial boundary (MEB) value from the ground and space weather data;
a second prediction generator for predicting a polar cap potential (PCP) value from the ground and space weather data;
an AL and AU prediction generator for predicting AL and AU values from the ground and space weather data;
means for generating a time-varying Kp-related value from the MEB value;
an electric field pattern generator for determining time-varying electric field patterns from the Kp-related value, the PCP value, and the ground and space weather data;
a conductivity generator for determining conductivity values from the ground and space weather data and the time-varying Kp-related value; and an adaptive feedback generator for adaptively generating time-varying geomagnetic parameters from the conductivity values, the time-varying electric field patterns, and the predicted AL and AU values using feedback, the time-varying geomagnetic parameters indicating the forecasted geomagnetic events and resulting ionospheric currents.
15. The system of claim 14 wherein the processor generates and outputs the time-varying geomagnetic parameters indicating the forecasted geomagnetic events and resulting ionospheric currents to facilitate implementation of mitigating actions to minimize the effects of such forecasted geomagnetic events and resulting ionospheric currents.
16. The system of claim 14 wherein the processor generates and outputs the time-varying geomagnetic parameters indicating the forecasted geomagnetic events and resulting ionospheric currents with a predetermined lead time to the actual geomagnetic events and resulting ionospheric currents.
17. The system of claim 16 wherein the processor generates and outputs the time-varying geomagnetic parameters with a predictive lead time of about 45 minutes.
18. The system of claim 16 wherein each of the first and second prediction generators includes a neural network trained to provide the predicted MEB value and PCP
value, respectively, with a predictive lead time of about one hour.
value, respectively, with a predictive lead time of about one hour.
19. The system of claim 14 further including:
a data pre-processor for averaging a portion of the ground and space weather data using a predetermined data window; and the processor uses the averaged portion of the ground and space weather data to generate the time-varying geomagnetic parameters to indicate the forecasted geomagnetic events and resulting ionospheric currents at a time corresponding to a predetermined point in the data window.
a data pre-processor for averaging a portion of the ground and space weather data using a predetermined data window; and the processor uses the averaged portion of the ground and space weather data to generate the time-varying geomagnetic parameters to indicate the forecasted geomagnetic events and resulting ionospheric currents at a time corresponding to a predetermined point in the data window.
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|---|---|---|---|
| US08/583,428 US5673366A (en) | 1996-01-05 | 1996-01-05 | System and method for geomagnetic and ionospheric forecasting |
| US08/583,428 | 1996-01-05 |
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| US5796611A (en) * | 1994-10-04 | 1998-08-18 | Nippon Telegraph And Telephone Corporation | Weather forecast apparatus and method based on recognition of echo patterns of radar images |
| US7844517B2 (en) * | 1996-01-18 | 2010-11-30 | Planalytics, Inc. | System, method, and computer program product for forecasting weather-based demand using proxy data |
| US7031927B1 (en) | 2000-04-12 | 2006-04-18 | Strategic Weather Services | System, method, and computer program product for weather and terrestrial vegetation-based water renovation and management forecasting |
| US6816786B2 (en) * | 2000-04-18 | 2004-11-09 | Devrie S Intriligator | Space weather prediction system and method |
| US6356842B1 (en) | 2000-04-18 | 2002-03-12 | Carmel Systems, Llc | Space weather prediction system and method |
| US7155377B2 (en) * | 2001-08-23 | 2006-12-26 | Wisconsin Alumni Research Foundation | Method and system for calculating the spatial-temporal effects of climate and other environmental conditions on animals |
| US6910025B2 (en) * | 2001-11-20 | 2005-06-21 | Freescale Semiconductor, Inc. | Modeling behavior of an electrical circuit |
| US6952648B1 (en) * | 2003-02-04 | 2005-10-04 | Wsi Corporation | Power disruption index |
| US7184965B2 (en) * | 2003-10-29 | 2007-02-27 | Planalytics, Inc. | Systems and methods for recommending business decisions utilizing weather driven demand data and opportunity and confidence measures |
| DE102005038391A1 (en) * | 2004-10-01 | 2006-04-06 | Deutsches Zentrum für Luft- und Raumfahrt e.V. | Determination of an atmospheric state |
| JP2006155404A (en) * | 2004-11-30 | 2006-06-15 | Toshiba Corp | Time information extraction device, time information extraction method and time information extraction program |
| US20060229813A1 (en) * | 2005-03-30 | 2006-10-12 | Tobiska William K | Ionospheric forecast system (IFS) |
| US20060293980A1 (en) * | 2005-06-23 | 2006-12-28 | Planalytics, Inc. | Weather-based financial index |
| US7752106B1 (en) | 2005-07-19 | 2010-07-06 | Planalytics, Inc. | System, method, and computer program product for predicting a weather-based financial index value |
| US8181511B2 (en) * | 2008-07-18 | 2012-05-22 | Meier Robert R | Method and system of imaging electrons in the near earth space environment |
| US8428916B2 (en) | 2008-10-07 | 2013-04-23 | Los Alamos National Security, Llc | Modeling of the radiation belt megnetosphere in decisional timeframes |
| WO2011088473A2 (en) * | 2010-01-18 | 2011-07-21 | The Regents Of The University Of California | System and method for identifying patterns in and/or predicting extreme climate events |
| US9037413B1 (en) | 2010-10-12 | 2015-05-19 | Exelis, Inc. | System and method of visualization of species characterization, dynamics and stratification of the magnetosphere |
| US8616052B1 (en) | 2010-10-12 | 2013-12-31 | Exelis, Inc. | System and method of visualization of species characterization, dynamics and stratification of the magnetosphere |
| CN104484704A (en) * | 2014-12-22 | 2015-04-01 | 国家电网公司 | Wind power plant theoretical power determination method based on BP neural network |
| CN105160060B (en) * | 2015-07-17 | 2019-02-05 | 中国电力科学研究院 | A kind of wind power plant theoretical power (horse-power) based on actual power curve matching determines method |
| US10371862B2 (en) * | 2017-02-15 | 2019-08-06 | International Business Machines Corporation | Geomagnetic storm warning |
| US10423181B2 (en) * | 2017-03-31 | 2019-09-24 | International Business Machines Corporation | Geomagnetically induced potential compensation |
| CN109948118B (en) * | 2019-03-29 | 2022-12-06 | 国家卫星气象中心(国家空间天气监测预警中心) | Method, system, medium and terminal for correcting geomagnetic activity index based on MSISE00 atmospheric model |
| CN111752966B (en) * | 2020-06-09 | 2023-11-24 | 中国人民解放军火箭军工程大学 | Earth change magnetic field disturbance analysis system and method |
| CN112016245A (en) * | 2020-08-13 | 2020-12-01 | 五邑大学 | Magnetic storm prediction method and device based on self-attention deformation network and storage medium |
| CN112257847A (en) * | 2020-10-16 | 2021-01-22 | 昆明理工大学 | Method for predicting geomagnetic Kp index based on CNN and LSTM |
| CN116882594B (en) * | 2023-09-06 | 2023-12-01 | 数字太空(北京)智能技术研究院有限公司 | Geomagnetic ap index medium-term forecasting method based on limited recursion and deep learning |
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| US5444820A (en) * | 1993-12-09 | 1995-08-22 | Long Island Lighting Company | Adaptive system and method for predicting response times in a service environment |
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