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University of Arizona Institute of Atmospheric PhysicsPage 1
A Comparison of the EOF Modes for Lightning Generated Electromagnetic Waveform
Risetimes and a Leaky Integration of the NCEP Stage-IV Precipitation Analysis.
William Scheftic (Graduate Student, Atmospheric Sciences, University of Arizona)
Collaborators on overall project:Kenneth L. Cummins and E. Philip Krider (Atmospheric Sciences,
University of Arizona)Ben K. Sternberg (Geological & Geophysical Engineering
and Electrical & Computer Engineering, University of Arizona)David Goodrich, Susan Moran, and Russell Scott (USDA
Southwest Watershed Research Center)
University of Arizona Institute of Atmospheric PhysicsPage 2
Outline
• Motivation and Background
• Technical Basis
• Brief Explanation of Lightning Method
• Data Preparation
• Eigen Analysis and Results
• Summary
University of Arizona Institute of Atmospheric PhysicsPage 3
Current Methods of Estimating soil moisture• In Situ soil moisture probes
• Good time resolution and vertical resolution from 1 to 2 meters.
• No expansive network of such measurements, point estimates
• Satellite-Based Near-surface (few cm) measurements (e.g. AMSR-E).
• High spatial and moderate temporal resolution.
• GRACE satellites measure total column changes of weight for the Earth
• Resolution around 400 km every 30 days
• Land Surface Models (LSMs)
• Estimates soil moisture with depth based on estimated precipitation, evaporative flux and runoff.
Motivation and Background
University of Arizona Institute of Atmospheric PhysicsPage 4
Focus of this talk:New approach using Lightning in conjunction with the National Lightning Detection Network (NLDN).
• Measurements may be possible with depth of a few meters along lightning to sensor path.
• During periods of frequent lightning spatial and temporal resolution of soil moisture estimate may be excellent.
• Goal is to achieve a national dataset with approximately 30 km spatial resolution.
Motivation and Background
University of Arizona Institute of Atmospheric PhysicsPage 5
Technical Basis: Effect of finite conductivity on surface-wave signal propagation
• At a given frequency complex attenuation (magnitude and phase change) is proportional to distance traveled and inversely proportional to conductivity (Norton 1937).
University of Arizona Institute of Atmospheric PhysicsPage 6
Lightning Background
2 3 4 – 6 7 - 13 14
17 – 33 ms ... 50 – 83.5 ms … 216 – 233 ms
23:42:47.542 ... No NLDN
Report … 23:42:47.762
Cloud-to-ground lightning electromagnetic fields: (b) First, and (c) Subsequent strokes
University of Arizona Institute of Atmospheric PhysicsPage 7
Rise-time determined by the highest frequencies
2 3 4 – 6 7 - 13 14
17 – 33 ms ... 50 – 83.5 ms … 216 – 233 ms
23:42:47.542 ... No NLDN
Report … 23:42:47.762
University of Arizona Institute of Atmospheric PhysicsPage 8
Propagation Animation - 75->450 km distanceConductivity = 5 mS/m
55 56 57 58 59 60 61 62 63 64 651
0.83
0.67
0.5
0.33
0.17
0.0
0.858
xf75m
sensor_lpf m
6555m
106
fs
75 km
University of Arizona Institute of Atmospheric PhysicsPage 9
55 56 57 58 59 60 61 62 63 64 651
0.83
0.67
0.5
0.33
0.17
0.0
0.858
xf75m
sensor_lpf m
6555m
106
fs
75 km
Propagation Animation - 75->450 km distance
University of Arizona Institute of Atmospheric PhysicsPage 10
55 56 57 58 59 60 61 62 63 64 651
0.83
0.67
0.5
0.33
0.17
0.0
0.858
xf150m
sensor_lpf m
6555m
106
fs
150 km
Propagation Animation - 75->450 km distance
University of Arizona Institute of Atmospheric PhysicsPage 11
55 56 57 58 59 60 61 62 63 64 651
0.83
0.67
0.5
0.33
0.17
0.0
0.858
xf225m
sensor_lpf m
6555m
106
fs
225 km
Propagation Animation - 75->450 km distance
University of Arizona Institute of Atmospheric PhysicsPage 12
55 56 57 58 59 60 61 62 63 64 651
0.83
0.67
0.5
0.33
0.17
0.0
0.858
xf300m
sensor_lpf m
6555m
106
fs
300 km
Propagation Animation - 75->450 km distance
University of Arizona Institute of Atmospheric PhysicsPage 13
55 56 57 58 59 60 61 62 63 64 651
0.83
0.67
0.5
0.33
0.17
0.0
0.858
xf375m
sensor_lpf m
6555m
106
fs
375 km
Propagation Animation - 75->450 km distance
University of Arizona Institute of Atmospheric PhysicsPage 14
55 56 57 58 59 60 61 62 63 64 651
0.83
0.67
0.5
0.33
0.17
0.0
0.858
xf450m
sensor_lpf m
6555m
106
fs
450 km
Propagation Animation - 75->450 km distance
University of Arizona Institute of Atmospheric PhysicsPage 15
Basic LTG Method
200 km LordsburgTucson
Williams
Window Rock
Yuma
• Select desired path for analysis
• Starting at a polygon that defines the lightning-observation region
• Ending at a sensor location (e.g., Lordsburg; Williams)
• Evaluate risetime of lightning waveform measured by the selected sensor.
• Convert risetime to apparent electrical conductivity.
• RT ≈ d/σ
• Convert apparent conductivity to soil moisture
Polygon
PathSensor
Basic Lightning Method
University of Arizona Institute of Atmospheric PhysicsPage 16
Soil Moisture Proxy: NCEP Stage IV Precip
• Dataset: Hourly data at 5 km spatial resolution.
• Accumulated with 0.5% leak-out per hour.
University of Arizona Institute of Atmospheric PhysicsPage 17
Data Preparation: Risetime Aggregation
Risetime for All Negative First Strokes in Tucson Area (Recorded at Lordsburg)
12345678
7/14
7/19
7/24
7/29 8/3
8/8
8/13
8/18
8/23
8/28 9/2
9/7
9/12
Date (2005)
Ris
eti
me
[u
s]
University of Arizona Institute of Atmospheric PhysicsPage 18
• Created spatial grids of risetime and leaky precip over southern Arizona and New Mexico.
• 0.333° x 0.333° resolution.
• 31N to 35N and 111.833W to 107W.
• Created 8 two week periods with one week overlap.
• Only used leaky precip during lightning observations periods.
• Then averaged over the 8 time periods.
• Missing data in a grid was interpolated as an average of its neighbors.
• In analysis comparing anomaly of leaky precip with standardized anomaly of risetime.
Data Preparation: continued
University of Arizona Institute of Atmospheric PhysicsPage 19
• Then average leaky precip from each grid box to the lightning sensor, to simulate conductivity along the path.
Data Preparation: Path Averaging
University of Arizona Institute of Atmospheric PhysicsPage 20
• Performed SVD analysis on the risetime data, the leaky precip, and the path averaged leaky precip to yield space (EOFs) and time (PCs) eigenvectors.
• Tested correlation and significance of the principle components of each field with the principle components of the other fields.
• Eigenvalue Projection: In SVD analysis,• A = USVT
• If A is the risetime and B is the leaky precip then,
• The diagonal of U-1BVT-1 is related to the variance accounted for by risetime in the leaky precip.
• If the diagonals can explain a similar amount of variance as the eigenvalues of Leaky Precip, then there is reason to believe the principle components are explaining the same phenomenon.
Eigen Analysis: Methods Used
University of Arizona Institute of Atmospheric PhysicsPage 21
Percent Variance Explained by Risetime PC
0%
10%
20%
30%
40%
50%
60%
1 2 3 4 5 6 7 8
Principal Component
% V
aria
nce
Exp
lain
ed
Percent Variance Explained by Path Ave Leaky Pcp PC
0%
20%
40%
60%
80%
100%
120%
140%
1 2 3 4 5 6 7 8
Principal Component
% V
aria
nce
Expl
aine
d
Percent Variance Explained by Leaky Pcp PC
0%
20%
40%
60%
80%
100%
120%
140%
1 2 3 4 5 6 7 8
Principal Component
% V
aria
nce
Expl
aine
d
Eigen Analysis: Eigenvalues
University of Arizona Institute of Atmospheric PhysicsPage 22
Eigen Analysis: 1st PC – Leaky Pcp
University of Arizona Institute of Atmospheric PhysicsPage 23
Eigen Analysis: 1st PC – Path Averaged
University of Arizona Institute of Atmospheric PhysicsPage 24
Eigen Analysis: 1st PC - Risetime
University of Arizona Institute of Atmospheric PhysicsPage 25
Eigen Analysis: PC Correlations
RTPC2 RTPC3 PathPC1 PathPC2 PathPC3 LkyPC1 LkyPC2 LkyPC3
RTPC1 0 0 -0.9204 -0.3002 -0.1054 -0.9649 -0.1244 0.0836
RTPC2 0 0.1724 -0.8185 0.5149 0.0141 -0.7256 -0.6667
RTPC3 0.292 -0.4832 -0.7537 0.1405 -0.6463 0.6259
PathPC1 0.0005 -0.0003 0.9735 -0.2205 0.0082
PathPC2 0.0006 0.2189 0.9395 0.2186
PathPC3 0.0495 0.0838 -0.7949
LkyPC1 0.0001 0.0012
LkyPC2 -0.001
University of Arizona Institute of Atmospheric PhysicsPage 26
• Recall we are seeing how the PCs of risetime project onto the Precipitation datasets.
• For leaky precip the projection is about 35% of the original eigenvalue for the 1st PC
• For path averaged leaky precip its around 40%
• Don’t want to infer too much from this, but it seems the variability in the 1st PC of leaky precip can be explained by risetime variability.
Eigen Analysis: Eigenvalue Projections
Leaky Original PC % Explained RT Proj.
PC1 58389.3 73.9% 20431.3
PC2 13542.3 17.1% 120.7
PC3 2820.1 3.6% 0.2
PC4 1507.0 1.9% 0.1
PC5 1034.2 1.3% 53.3
PC6 806.0 1.0% 7.6
PC7 499.0 0.6% 0.6
PC8 384.7 0.5% 43.7
Path Original PC % Explained RT Proj.
PC1 32450.6 80.7% 13138.3
PC2 4826.5 12.0% 103.2
PC3 1139.1 2.8% 29.3
PC4 589.5 1.5% 2.4
PC5 433.9 1.1% 2.8
PC6 307.6 0.8% 9.2
PC7 285.6 0.7% 1.4
PC8 169.5 0.4% 31.3
University of Arizona Institute of Atmospheric PhysicsPage 27
Limitations
• Other factors affect conductivity changes• Soil temperature, soil salinity, conductivity gradient over a
several-meter depth
• The size of the lightning region being analyzed determines how similar lightning to sensor paths really are
• Rise-time effect can only be observed over distances of at least 50 km, given ~10 mS/m conductivity
• No perfect set of wide-area data for validation• Stage IV precip based on radar data with rain gauge correction.• Soil moisture guages are point-specific• Land-surface models are only as good as their input.
• Must have lightning!!• This really creates problems with this type of analysis (missing
data, dry periods)
University of Arizona Institute of Atmospheric PhysicsPage 28
Summary and Future Work
• Norton model shows that a change in conductivity or distance at a given frequency has a well-behaved change in the attenuation of a wave propagating along the surface• The risetime of lightning is one phenomenon where this effect is seen.• We’ve shown that a decrease in lightning risetime is related to an increase in soil moisture.
• Although, the eigenvalue analysis wasn’t significant, there definitely seems to be a qualitatively interesting and physically valid reason for the 1st PC.
• Try Rotated EOF analysis, to see if localized spatial patterns of risetime and leaky precip agree more.
• We are working on a general method for characterizing conductivity changes for any given path based on soil characteristics.• Also, we need to further examine how other factors (soil temperature, ice content, etc) effect the conductivity of soil.• Develop methods for producing gridded spatial output of conductivity changes.• Analyze lightning data in different locations and times.
University of Arizona Institute of Atmospheric PhysicsPage 29
Supporting Material
University of Arizona Institute of Atmospheric PhysicsPage 30
Conductivity vs. NARR Soil Moisture (0-10 cm)(weighted count > 35) y = 0.026x - 0.3165
17
18
19
20
21
22
23
07/07 07/17 07/27 08/06 08/16 08/26 09/05 09/15
Date (2005)
(100
/RT
) [1
00/μ
s]
0.126
0.1460.166
0.1860.206
0.226
0.2460.266
Vo
l. S
oil
Mo
ist.
[f
ract
ion
]
condc
soil0_10
Soil Moisture Vs. Conductivity Relationship (With Signal Strength Slope Correction)
y = 0.0247x - 0.2907
R2 = 0.5754
0.13
0.15
0.17
0.19
0.21
0.23
0.25
0.27
17 18 19 20 21 22 23 24 25
100 / Risetime [100/μs]
Vo
l. S
oil
Mo
ist.
Previous Results