11 The Cryosphere Team Members  Cryosphere Application Team  Jeff Key (Lead; STAR/ASPB) »Peter Romanov (CREST) »Don Cline (NWS/NOHRSC) »Marouane Temimi (CREST) AWG Cryospere Team Chair: Jeff Key AWG Cryospere Team Chair: Jeff Key (Option 2)  Ice Cover and Concentration (Option 2)  Yinghui Liu (Lead; CIMSS)  Xuanji Wang (CIMSS)  Jeff Key (STAR)  Marouane Temimi (CREST) (Option 2)  Ice Motion (Option 2)  Yinghui Liu (Lead; CIMSS)  Jeff Key (STAR)  Xuanji Wang (CIMSS) (Option 2)  Ice Age/Thickness (Option 2)  Xuanji Wang (Lead; CIMSS)  Jeff Key (STAR)  Yinghui Liu (CIMSS)  Fractional Snow Cover (baseline)  Don Cline (Lead; NWS/NOHRSC)  Tom Painter (JPL)  Andy Rost (NWS/NOHRSC)  Snow Depth (Option 2)  Peter Romanov (Lead; CREST)  Cezar Kongoli (CICS)

22 Snow and Ice in North America

3 GOES-R AWG Cryosphere Team: ABI Snow Depth Algorithm June 15, 2011 Presented By: Peter Romanov 1 1 NOAA-CREST City College of New York In collaboration with Cezar Kongoli, CICS University of Maryland

4 Outline  Executive Summary  Algorithm Description  ADEB and IV&V Response Summary  Requirements Specification Evolution  Validation Strategy  Validation Results  Summary

5 Executive Summary  ABI Snow Depth Algorithm generates the Option 2 Snow Depth product.  Version 4 was delivered in November Version 5 was delivered in early June ATBD (100%) is on track for delivery in July 2011  The Snow Depth algorithm was modified from Version 4 to Version 5 to reduce seasonal bias in snow depth retrievals. The new algorithm was tested with GOES Imager data. The accuracy of the algorithm is within specifications.  Routine validation tool has been designed and is being developed. First delivery is in September  The algorithm is being implemented by AIT. So far four MODIS processed granules are available from AIT. Preliminary validation shows compliance with specifications.

6 Algorithm Description

7 Algorithm Summary  Snow depth is derived from snow cover fraction. An empirical formula relating snow depth and snow fraction is applied.  The snow depth – snow fraction relationship was established using snow fraction estimates from current GOES Imager data matched with snow depth measurements at ground-based stations.  Snow depth is derived over plain non-forested areas (grassy plains) in clear sky conditions.  Product has quality flags for elevation, forested terrain, satellite zenith angle and solar elevation

8 Version 4 Snow Depth Algorithm Snow Depth algorithm : Depth = exp (a * Fraction) – 1 Depth: Snow depth in cm Fraction: Snow fraction in % a : coefficient (in Version 4: a = 0.033) However: In early winter fresh snow more effectively masks low-level vegetation than old/melting snow in the end of winter Therefore snow depth –snow fraction relationship should change throughout the winter season

9 Snow Depth vs Snow Fraction January a= November a= Depth = exp (a * Fraction) –1  Snow depth-snow fraction statistics was collected during November 2010 – April 2011 over Great Plains.  The value of a of the best fit increases as the winter season progresses March a=

10 Version 5 Snow Depth Algorithm Snow Depth – Snow fraction formula is the same : Depth = exp (a * Fraction) – 1 But a increases linearly with time during the winter season (Nov1 to Apr 15) a = s* N d + c c = s = N d : number of days since November 1

11 Other Algorithm Changes from 80% to 100%  Metadata added.  Quality flags added.

12 ADEB and IV&V Response Summary General Comment 1: Insufficient validation Response: The algorithm has been thoroughly validated using several thousand matched GOES-based retrievals and in situ observations. The AIT product has not been validated since it is not available. General Comment 2: Continue improvement of algorithms Response: Accepted. We do continue working on the improvement of the Snow Depth algorithm

13 ADEB and IV&V Response Summary General Comment 3: Make better use of the GOES advantages: high temporal response, varying solar angle, time continuity, fixed FOV, etc. Response: This will be considered in the further improvement of the algorithm However sensible improvements may be expected only for daily (not hourly) products General Comment 4: Graceful degradation is not addressed Response: Accepted. However the term should be more clearly defined and possible scenarios of graceful degradation should be agreed upon.

14 ADEB and IV&V Response Summary Specific Comment: Investigate cases when snow depth retrieval errors were 5-10 cm. Response: 5-10 cm retrieval errors are not bad. They are within the product accuracy specifications. We are mostly looking at factors causing larger, over cm errors. These factors are - Errors in cloud detection - Subresolution water bodies - Urban areas, snow removal - Large horizontal inhomogeneity of the snow pack - Errors in snow depth reports from ground stations

15 Requirements ProductAccuracyPrecisionRangeLatency / Refresh Rate Horizontal resolution Snow Depth 9 cm15 cm0-27 cm60 / 60 min2 km Requirements Evolution: Original requirement: 30% accuracy Year 2009: 9 cm accuracy and 9 cm precision Year 2011: 9 cm accuracy and 15 precision Reasons for changing specifications are unknown. The team has never requested any changes.

16 Validation Strategy

17 Validation Approach  ABI snow depth retrievals will be matched and directly compared to synchronous snow depth measurements at ground stations.  Primary source for validation are snow depth reports from WMO and US Cooperative network stations. WMO station data are acquired through GTS or from NCDC, US Coop station data are obtained from NOAA CPC or NCDC  The number of daily match-ups may reach ~ 400 in the middle of winter season.  Comparison will be performed with NOHRSC snow depth model output on case to case basis.

18 Validation Approach  Output of the validation system will include »Maps of snow depth with surface observation data overlaid »Daily statistics of snow depth retrieval accuracy over NH »Monthly and seasonal statistics by station »Daily time series of accuracy estimates (bias and precision)

19 Validation Results

20 GOES Snow Depth vs In Situ Data Winter of  Performed over Great Plains  COOP and WMO SD data  Up to 400 match ups daily  Smaller bias with v.5 algorithm  Increasing disagreement towards the end of winter season is due to larger horizontal inhomogeneity of the snow pack Bias Within Specifications StDev

21 Snow Depth Retrieval Statistics Winter of Snow Depth Error, cm Number Frequency Depth < 60 cm: N=8091 Mean Bias=-1.1 cm MAE=7.8 cm RMS Error=11.0 cm Depth < 30 cm N=6611 Mean Bias=1.7 cm MAE=6.2 cm RMS Error=8.5 cm

22 Daily Statistics Date: Ntot:136 Accuracy: -1.8 cm (all depths) 1.2 cm (depth ≤ 30 cm) Precision: 10.4 cm (all depths) 6.1 cm (depth ≤ 30 cm) Correlation: 0.63 Date: Ntot:363 Accuracy: -2.3 cm (all depths) 5.9 cm (depth ≤ 30 cm) Precision: 12.1 cm (all depths) 10.1 cm (depth ≤ 30 cm) Correlation: 0.54 Jan 1, 2011 Feb 9, 2011

23 Statistics by Station Winter Season

24 Validation over common data set (Framework validation) Terra MODIS data at 1750Z on Snow fraction derived by AIT with GOES-R ABI algorithm Snow depth has been derived from snow fraction

25 Derived snow depth agree well to station Snow Depth data Validation over common data set (Framework Validation), cont’d Only one MODIS image has been processed by AIT to derive SF and SD More cases are needed to better assess the retrieval accuracy

26 Validation Results Summary Accuracy (spec)Precision (spec) Snow Depth (GOES) winter season Depth < 30 cm 1.7cm (9 cm)8.5 cm (15 cm)

27 Summary  The ABI Snow Depth Algorithm is based on an empirically derived snow fraction – snow depth relationship  The relationship is valid and is applied over flat and non-forested areas (plain grassy areas).  Algorithm testing is performed with current GOES Imager data.  Algorithm has been validated with synchronous surface observations and was confirmed to meet the requirements.  Product accuracy has yet to be evaluated  Version 5 of the algorithm is ready for delivery. 100% ATBD is being prepared.