A Microwave Retrieval Algorithm of Above-Cloud Electric Fields

A Microwave Retrieval Algorithm of Above-Cloud Electric Fields Michael J. Peterson The University of Utah Chuntao Liu Texas A & M University Corpus Christi Douglas Mach Global Hydrology and Climate Center Wiebke Deierling Christina Kalb National Center for Atmospheric Research

The Global Electric Circuit (GEC) Ionosphere 250 kv Wilson Currents Fair Weather Currents Ground 0 V

How is the GEC Studied? Direct E field observations Limited domain and sample size Continuous global observations Thunderstorms only

Goal To create an algorithm that can estimate above-cloud electric fields that uses commonly-available global satellite products o Passive microwave - SSMI - TMI - GMI o Radar - TRMM PR - GPM DPR

Objectives To provide a unique tool for examining: o Individual cases and global electricity o Long-term variations in global electricity o Relative contributions of different cloud types to the GEC To provide validation for the FESD:ECCWES effort

Theoretical Basis Ice Particle Collisions Hydrometeor Charging Charge Separation Wilson Currents GEC

Theoretical Basis Ice Particle Collisions Hydrometeor Charging Charge Separation Wilson Currents Collision Frequency... Ice Concentration GEC

Theoretical Basis Ice Particle Collisions Hydrometeor Charging Charge Separation Wilson Currents Collision Frequency... Ice Concentration 37 GHz and 85 GHz Passive Microwave Observations GEC

High Altitude Aircraft Version NASA ER-2 o Advanced Microwave Precipitation Radiometer (AMPR) o Lightning Instrument Package (LIP) - 3D electric field vector 4 field campaigns: CAMEX-3, CAMEX-4, TCSP, TRMM- LBA AMPR and LIP observations are used to construct the algorithm and assess its validity

How the Algorithm Works 20 km Coulomb s law: E i = c q i r i 2 10 km q i 5 km 0 km 200 K

How the Algorithm Works 20 km 10 km 5 km 0 km 260 K 200 K 150 K 200 K 250 K 270 K 290 K 300 K 300 K 300 K 280 K 250 K 250 K 270 K 300 K

How the Algorithm Works 20 km 10 km q net i 5 km 0 km 260 K 200 K 150 K 200 K 250 K 270 K 290 K 300 K 300 K 300 K 280 K 250 K 250 K 270 K 300 K

How the Algorithm Works 20 km h i = f (T b i ) q net i = f (T b i ) 10 km 5 km q net i h i 0 km 260 K 200 K 150 K 200 K 250 K 270 K 290 K 280 K 250 K 250 K 270 K

How the Algorithm Works 20 km Coulomb s law: r i proxye i = c q net i r i 2 h i = f (T b i ) q net i = f (T b i ) 10 km 5 km q net i h i 0 km 260 K 200 K 150 K 200 K 250 K 270 K 290 K 280 K 250 K 250 K 270 K

Algorithm Performance Over Land

Algorithm Performance over Land

Example Case

Missed Event Case

35.60 36.00 36.00 35.60 E (V/m) False Alarm Case -67.70-67.50-67.30-67.10-66.90-67.70-67.50-67.30-67.10-66.90 ER-2 Track 10 V/m E Max. 100 V/m 160 V/m K 300 285 270 255 240 225 210 195 180 165 150 600 V/m 800 V/m 200 150 100 50 0 0 20 40 60 80 Flight Track Distance (km) LIP E Field Strength Estimated ER-2 E Field Strength

Overall Performance over Land Error < 100% Missed Events False Alarms 37 GHz Shower clouds > 100 V/m Storm clouds > 100 V/m 85 GHZ Shower clouds > 100 V/m Storm clouds > 100 V/m 40.3 % 53.2 % 6.4 % 41.7 % 47.1 % 11.2 % 68.2 % 17.5 % 14.4 % 69.5 % 7.4 % 23.2 %

Satellite Version Tropical Rainfall Measuring Mission (TRMM) o TRMM Microwave Imager (TMI) o Precipitation Radar (PR) o Lightning Imaging Sensor (LIS) Designed to take advantage of unique sensor package o Radar-based estimate of charge height o Radar-based stratiform/convective partitioning

Satellite Version 20 km 10 km h i = max ht of 30 dbz q net i = f (T b i ) 5 km q net i h i 0 km 260 K 200 K 150 K 200 K 250 K 280 K 250 K 250 K 270 K

Comparison with LIS Lightning 1998 Distribution of LIS Lightning Flashes -90 0 90 30 30 1.09 % 0.40 10 10-10 -10-30 -30-90 0-90 0 90 0.15 0.05 0.02 0.01 1998 Distribution of Total Proxy E (stratiform scaling: 10%) 90 30 30 0.50 0.18 10 10-10 -10 0.07 0.02-30 -30 0.01-90 0 90 0.00 %

Fraction of Global Mean Fraction of Global Mean Fraction of Global Mean Fraction of Global Mean Comparison with LIS Lightning 2.5 2.0 1.5 1998 Diurnal LIS Lightning Distribution 1.5 1.0 1.0 0.5 0.0 0 3 6 9 12 15 18 21 24 Local Time Global N. America S. America Africa 0.5 0.0 0 3 6 9 12 15 18 21 24 UTC Time E. Asia Carnegie Curve 1998 Diurnal Proxy E Distribution over Land (stratiform scaling: 10%) 2.0 1.5 1.0 0.5 0.0 0 3 6 9 12 15 18 21 24 Local Time Global N. America S. America Africa 1.2 1.0 0.8 0.6 0.4 0.2 0.0 0 3 6 9 12 15 18 21 24 UTC Time E. Asia Carnegie Curve

Conclusions The high altitude aircraft version can produce reasonable estimates of electric fields above convective clouds and clouds with significant electric fields The algorithm in its present form cannot adequately characterize electric fields above stratiform clouds and convection near large stratiform regions o Particularly a problem for oceanic regions and mature MCS s over land Passive microwave estimates of global electricity over land lead to similar spatial and temporal distributions compared to LIS lightning frequency

Next Steps Apply new dynamic stratiform scaling factor to prevent stratiform bias in convective pixel calculations (6,000 TRMM orbits processed) Incorporate ground-based radar observations into the high-altitude aircraft dataset Explore the feasibility of using a microwave-based convective/stratiform partitioning scheme Determine whether a combined 85 GHz/37 GHz charge proxy would have more skill than considering each frequency independently Apply algorithm to entire 16-year TRMM dataset

Proxy E E Transfer Functions

Stratiform Scaling 1998 Distribution of Total Proxy E (stratiform scaling: 100%) -90 0 90 30 30 0.41 % 0.15 10 10-10 -10 0.06 0.02 0 90-90 0 90 0.00 30-90 -30-30 0.01 0.53 30 1998 Distribution of Total Proxy E (stratiform scaling: 0%) 0.20 10 10-10 -10 0.07 0.03-30 -30 0.01-90 0 90 0.00 %