Snowfall Detection Using ATMS Measurements

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1 Snowfall Detection Using ATMS Measurements Cezar Kongoli, Huan Meng, Ralph Ferraro and Jun Dong CICS/ESSIC, University of Maryland and NOAA/NESDIS/STAR Nov. 7, 2013 CICS-MD Science Meeting

2 AMSU Heritage Snowfall Detection Key Features: Decision-tree binary classification: Combination of channels in the window, oxygen and water vapor absorption to filter out snow on the ground; Operational AMSU Snowfall & Rain Rate (Ferraro et al. 2005; Meng et al., 2012) + Snowfall Detection (Kongoli et al., 2003) TB53L used as temp. filter - cold snow events missed Misses of cold snowfall Some false alarm still noticeable Retrieval Example: IMS Snow before and after the event

3 New ATMS Snowfall Detection Enhanced Methodology: Match-up training data against hourly surface reports Probability of snowfall replaces binary algorithm Logistic Regression (LR) replaces decision tree Evaluation of the utility of Principal Components (PC) coupled with LR Evaluation of the utility of ancillary data to reduce false positives Only Snowfall Rate > 0.1 mm/hr considered Matchup Dataset Period: January early March 2013 Match-up with in-situ stations Note: Some rain mis-identified as snowfall possible as indicated by very high Prec Rate filtered out by setting a measured surface temperature threshold at 2.0 C to separate rain from the nowfall sample

4 ATMS Channel Characteristics Channel Frequency [GHz] Footprint at nadir[km x km] Footprint at edge of scan [km x km] V 74.8 x x V 74.8 x x H 31.6 x x x x H 31.6 x x ±0.115 V 31.6 x x x x x x x x x x ± x x ± ± x x ± ± x x ± ± x x ± ± x x V 31.6 x x H 15.8 x x ± x x ± x x ± x x ± x x ± x x 30 Some differences between ATMS and AMSU/MHS 1. ATMS one single instrument design 3x weight less 2. ATMS swath 2500 km versus 2200 km for AMSU - Near global coverage 3. ATMS 22-channel versus AMSU/MHS 20 - Oxygen Absorption Channel 4 - WV Channels 19 and 21 - WV Channels are referred to hereafter as 176, 179, 180, 181 and 182 GHz. 4. Polarization difference Of particular significance to snow retrievals is the QH polarization of ATMS channels 3,5 and17 versus QV polarization in AMSU/MHS

5 AMSU no-snowfall sample correlation statistics Limb-corrected oxygen absorption channel at 53.6 GHz TB53L (peaking at about 4 km) most correlated with surface temperature, followed by window TB157, water vapor TB190 and windows TB89 & TB50 Regression Standard Error with TB53L = 4.5 K & Multiple Regression Standard Error = 4 K. Correlatio n for no snowfall cases Twet 0.85 Tmp Twet TB23 TB31 TB50 TB89 TB157 TB182 TB180 TB190 TB TB TB TB TB TB TB TB TB53L

6 AMSU snowfall sample correlation statistics TB53L still most correlated with surface temperature, followed by window TB89 &TB50. Correlation with water vapor TBs very small. Correlations with surface temperature are smaller than for no snowfall Correlation among water vapor (wv) channels and between window & wv TB176 and TB180 increases compared to no-snowfall due to precipitation effects on these channels Correlatio ns for Snowfall Tmp Twet TB23 TB31 TB50 TB89 TB157 TB182 TB180 TB190 Twet 0.94 TB TB TB TB TB TB TB TB TB53L

7 ATMS no-snowfall sample correlation statistics TB53L using AMSU limb-correction coefficients most correlated with surface temperature, followed by water vapor TB176 and window TB165/TB89. Correlations with surface temperature lower than AMSU Tmp Twet TB23 Tb31 TB50 TB89 TB165 TB176 TB179 TB180 TB181 TB182 Twet 0.72 TB Tb TB TB TB TB TB TB TB TB TB53L

8 ATMS snowfall sample correlation statistics TB53L most correlated to surface temperature, with other channel correlations dropping significantly as compared to no snowfall sample Correlations among the wv channels increase compared to no snowfall sample but not correlations among TB89/TB165 and wv channels Tmp Twet TB23 Tb31 TB50 TB89 TB165 TB176 TB179 TB180 TB181 TB182 Twet 0.88 TB Tb TB TB TB TB TB TB TB TB TB53L

9 Scatterplots between surface temperature and TBs for snowfall (red) and no-snowfall (blue) cases ATMS AMSU/MHS

10 PCA analysis Component (correlations with TBs) TB TB TB TB TB TB TB Total Variance Explained Component Initial Eigenvalues Extraction Sums of Squared Loadings Total % of Variance Cumulative % Total % of Variance Cumulative % Extraction Method: Principal Component Analysis. Component Score Coefficient Matrix Component TB TB TB TB TB TB TB Extraction Method: Principal Component Analysis.

11 Three PCs TB176, TB180, TB179

12 Probability of Detection via Logistic Regression - Results TB89, TB165, TB176, TB180, coslza Step 1 Observed snowcode Classification Table a snowcode 0 1 Predicted Percentage Correct Overall Percentage 77.2 a. The cut value is.500 Step 1 PCA: Factor1, Factor2, Factor 3 Observed snowcode Classification Table a snowcode 0 1 Predicted Percentage Correct Overall Percentage 74.9 a. The cut value is.500 Step 1 Observed snowcode Classification Table a snowcode 0 1 Predicted Percentage Correct Overall Percentage 78.4 a. The cut value is.500 TB176, TB180, coslza PCA Component-based algorithm is less robust than TB-based algorithms

13 Application of temperature and humidity filters reduces false alarm from 22% down to only 5% while minimally reducing classification rate from 78% down to 75% - Validation and final algorithm selection for optimal performance is work in progress ALL Data TB53L between 245 and 258 Kelvin Logistic Regression model TB176, TB179, TB180, cosza Humidity > 75%

14 Some final thoughts on rain-snow classification. What was presented dealt with the problem of separating snowfall from no snowfall how about separating snowfall from rain? That is routinely done via (modeled) surface temperature. Digging into past data - NOAA-15-16,-17 satellites it shows that AMSU has the potential to separate snow from rain via a combination of surface and oxygen absorption channels. At WV channels, the difference between the rain and snow signal is reduced substantially. ATMS potential also needs to be explored.