Tom Hopson, NCAR (among others) Satya Priya, World Bank Flood forecasting precipitation products as part of the project: Development of Flood Forecasting for the Ganges and the Brahmaputra Basins using satellite based precipitation, ensemble weather forecasts, and remotely-sensed river widths and height Tom Hopson, NCAR (among others) Satya Priya, World Bank

Outline Observed Precipitation products Ensemble Precipitation Forecasts from Global Forecast Systems Biases and Correlations amongst the Products

Satellite Products Satellite products are available as soon as each 24-hour accumulation period is completed. Product Name Institution Country Sensor Types Resolution TRMM NASA USA Passive microwave, Infrared 0.25 deg GSMAP JAXA Japan 0.1 deg CMORPH NOAA ~0.25 deg Our NCAR merged product is a simple average of the available satellite products

Measurement of Precipitation -- Rain Gauges Rain gauge estimates: NOAA CPC and WMO GTS 0.5 X 0.5 spatial resolution; 24h temporal resolution approximately 100 gauges reporting over the Ganges-Brahmaputra 24hr reporting delay

Comparison of Precipitation Products: TRMM Note: TRMM and CMORPH use same satellite information, but different estimation algorithms

Movie of one day’s “merged” rainfall we are using for this project – blended precipitation estimates from NOAA CMORPH, NASA 3B4RT, and JAXA to reduce overall error. If the individual products were compared, there are clear differences in locations and intensities of rainfall at finer scales, although the general large-scale features are generally in common.

Outline Observed Precipitation products Ensemble Precipitation Forecasts from Global Forecast Systems Biases and Correlations amongst the Products

TIGGE Forecasts Forecasts are on 2 day delay from TIGGE (The International Grand Global Ensemble). Forecast Center Country / Region # of Ensemble Members Forecast Out to: Currently on Display ECMWF Europe 50 15 days Yes UKMO UK 11 7 days CMC Canada 20 16 days NCEP USA < Dec 2015 CMA China 14 No CPTEC Brazil MeteoFrance France 34 4.5 days JMA Japan 26 11 days BoM Australia 32 10 days KMA Korea 23 10.5 days Originally a project of THORPEX: a World Weather Research Programme project to accelerate the improvements in the accuracy of 1-day to 2-week high-impact weather forecasts.

Unique Datasets/Software Created Thorpex-Tigge UKMO CMC CMA ECMWF MeteoFrance NCAR NCEP JMA NCDC IDD/LDM HTTP Using the Thorpex-Tigge data for this project. Green dots show forecasting centers which we are using their forecasts for. FTP Archive Centre CPTEC Current Data Provider

Archive Status and Monitoring, Variability between providers

Early May 2011, floods in southwestern Africa

Early May 2011, floods in southwestern Africa -- examine ens forecasts … ECMWF 24hr precip

Early May 2011, floods in southwestern Africa -- examine ens forecasts … NCEP GEFS 24hr precip

Early May 2011, floods in southwestern Africa -- examine ens forecasts … ECMWF 5-day precip

Early May 2011, floods in southwestern Africa -- examine ens forecasts … NCEP GEFS 5day precip

Movie: ECMWF 24-hr forecasted daily rainfall over the Ganges and Brahmaputra basins showing the variability in all 50 ensemble members

Outline Observed Precipitation products Ensemble Precipitation Forecasts from Global Forecast Systems Biases and Correlations amongst the Products

Emily Riddle, Tom Hopson Bias and skill in rainfall forecasts over the Ganges and Brahmaputra river basins Emily Riddle, Tom Hopson Acknowledgements: Jenn Boehnert, Kevin Sampson, Will Cheng, Dan Collins, Dorita Rostkier-Edelstein

Bias in rainfall forecasts for the Ganges-Brahmaputra basin Observed August Rainfall (2011-2014) Weather forecasts show large regional biases in rainfall over these basins The models vary in their spatial representation of climatological rainfall, including interactions between rainfall and topographic features. These results highlight the importance of calibration and bias correction for these forecasts. August Average of 0-4 day lead forecasts (2011-2014) US NCEP Model Canadian Model UK Met Office Model European Model

Goals: Compare the seasonal cycles of rainfall in satellite observations and medium range forecasts from four numerical weather prediction (NWP) models over GBM catchments. Assess the skill of these models as a function of lead time (0-14 days).

Spatial and temporal scales: 28 Catchments 4696 Catchments Temporal: 24 hour accumulations: 0 to 10 day lead times 5 day accumulations: 0-4 day, 5-9 day, 10-14 day lead times

Assessing bias: Methodology Calculate 5-year climatologies from merged satellite precip (2011-2015) Calculate 5-year lead-based climatologies from the 4 NWP models (2011-2015). Smooth climatologies with 11 day running average and regrid to catchments using correspondence files. Calculate bias as the difference between the model and satellite climatologies. A few caveats: Satellite precip has errors Using control run rather than ensemble mean for this analysis Relatively few years, so smoothing does not eliminate all interannual variability

August Average of 0-4 day lead forecasts (2011-2015) Satellite August Rainfall (2011-2015) August Average of 0-4 day lead forecasts (2011-2015) mm/day NCEP Model (USA) Canadian Model 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 UK Met Office Model ECMWF (European)

August Forecasts versus merged satellite precip (2011 – 2015) CANADA NCEP ECMWF UKMET Bias (mm/day)

Yamuna Ghagra Middle Ganges Manas ---- ECMWF ---- NCEP ---- CMC ---- UKMET ---- OBS Middle Brahamaputra Chambal Sone Meghna

Assessing forecast skill: Methodology Make climatologies from merged satellite precip (TRMM, CMORPH, JAXA) for 2011-2015. Calculate lead-based climatologies from Tigge forecasts (ECMWF, NCEP, UKMET, Canada) for 2011-2015. Calculate daily and 5-day precipitation anomalies for each lead time and the satellite product by subtracting off the lead-based climatologies. Calculate correlations between the anomaly time series for each catchment.

Correlations for JJA (2011-2015) averaged over all catchments Daily rainfall (lines) and Pentad rainfall (x’s) ---- ECMWF ---- NCEP ---- CMC ---- UKMET X’s: 5-day averages Lines: 24 hour averages

Spatial pattern of skill: JJA, 0-4 day forecast NCEP CANADA UKMET ECMWF Anomaly Correlations

Skill as a function of catchment area: JJA 0-4 day forecast Yarlung- Tsangpo Basin (Tibet)

Conclusions: Precipitation forecasts show large regional biases in rainfall, highlight the importance of calibration and bias correction. The models vary in their spatial representation of climatological rainfall, indicating differing interactions between rainfall and topographic features. Models with the smallest biases are not necessarily the ones with the best skill. The relationship between forecast skill and basin size is apparent in these data, though other geographic factors are clearly important as well.

Additional plots showing intercomparisons between observational products and forecasts

25th (bottom bar) 50th (circle) and 75th (top bar) percentile rainfall for observed and forecast rainfall products averaged over all catchments. 25th (blue) and 75th (red) percentiles are also shown for individual catchments for values that exceed / fall below the catchment averaged values. JAXA TRMM GAUGE NCEP UKMET CANADA Multi-Model Mean MERGED ECMWF Persistence

Intercorrelations between Observations and 24-hour forecasts for July and August Larger scale catchments Observations Forecasts GAUGE + MERGED Multi-Model Mean Persistence ECMWF CANADA MERGED NCEP UKMET JAXA TRMM GAUGE MERGED JAXA Observations TRMM GAUGE GAUGE + MERGED ECMWF NCEP CANADA Forecasts UKMET Multi-Model Mean Persistence

Intercorrelations between Observations and 24-hour forecasts for July and August Smaller scale catchments Observations Forecasts GAUGE + MERGED Multi-Model Mean Persistence ECMWF CANADA MERGED NCEP UKMET JAXA TRMM GAUGE MERGED JAXA Observations TRMM GAUGE GAUGE + MERGED ECMWF NCEP CANADA Forecasts UKMET Multi-Model Mean Persistence

Hit Rate and False Alarm Rate for 75th percentile precipitation events averaged over all catchments. Forecasts are for 0-24h precipitation. False Alarm Rate: % of observed average/low precip days that were incorrectly predicted to be high precip events (Best forecast: 0%, null forecast: 25%) Hit Rate: % of observed high precip events that were correctly forecast (Best forecast: 100%, null forecast: 25%) Verification product Gauge Merged Sat JAXA TRMM Merged + Gauge NCEP ECMWF CANADA UKMET Multi-Model Mean Persistence NCEP ECMWF CANADA UKMET Multi-Model Mean Persistence Forecast Product

Spatial Scale Dependence: 0-24h forecasts July and August 2011-2015 Uses multi-model mean for forecast Uses multi-product mean for observations Blue: Large scale catchments Red: Smaller scale catchments

Similar plots for 5-day averages

Intercorrelations between Observations and 5-day forecasts for July and August Larger scale catchments Observations Forecasts GAUGE + MERGED Multi-Model Mean Persistence ECMWF CANADA MERGED NCEP UKMET JAXA TRMM GAUGE MERGED JAXA Observations TRMM GAUGE GAUGE + MERGED ECMWF NCEP CANADA Forecasts UKMET Multi-Model Mean Persistence

Intercorrelations between Observations and 5-day forecasts for July and August Smaller scale catchments Observations Forecasts GAUGE + MERGED Multi-Model Mean Persistence ECMWF CANADA MERGED NCEP UKMET JAXA TRMM GAUGE MERGED JAXA Observations TRMM GAUGE GAUGE + MERGED ECMWF NCEP CANADA Forecasts UKMET Multi-Model Mean Persistence

Spatial scale dependence: 0 – 5 day forecasts: July and August 2011-2015 Uses multi-model mean for forecast Uses multi-product mean for observations Blue: Large scale catchments Red: Smaller scale catchments

Observations’ Take-Home Points satellite precip products internally highly correlated (no surprise) – however, correlation dependence on spatial scale still does imply some independent information. This “independent information” of the satellite products is seen by the increase (albeit slight compared to TRMM) in the highest satellite product-gauge correlation occurring for the satellite “merged” product. comparison with rain gauge: see increase in correlation both spatial (small to large catchment) and temporal (1-day to 5-day accumulations) scale also implies the skill of these products increase with scale as well, implying their utility increases as catchments increase in size. Highest correlations of the NWP with the gauge+merged product does also imply that both gauges and satellite products provide independent information.

NWP Take-Home Points significant skill increase over persistence, both small to large spatial and temporal scales – forecast information provides utility beyond just a good obs network can provide from a weather and hydrologic standpoint although arguably Canada had some of the lowest biases, seeing may have some of the lowest skill as well. Similarly, NCEP (most similar to NCMRWF operational model) also shows lower skill As with satellite/gauge intra- and intercomparisions, see increase in skill of all products with spatial and temporal scales. ECMWF coming out ahead of all 4 products (although they also seems to show some of the worst biases, interestingly enough).

NWP Take-Home Points (cont) Models themselves not that highly correlated. Multi-model (straight average) does show improvement over ECMWF although only slight Highest correlations observed with the multi-model highlights that the lack of correlation amongst the models means that their errors actually do at least partially off-set each other. Over-biases in all NWP (including multi-model) products relative to (almost) all “obs” products. Note: the cross-correlation of the different “observation” products (i.e. JAXA, TRMM, gauge) with the “merged-gauge” product is approximately 0.85, which provides an estimate on the upper bound on the possible correlation the NWP TIGGE products can achieve with the “merged-gauge” product. Therefore, the multi-model mean correlation of approximately 0.73 is, in fact, approaching this upper bound.

“I have a very strong feeling that science exists to serve human welfare. It’s wonderful to have the opportunity given us by society to do basic research, but in return, we have a very important moral responsibility to apply that research to benefiting humanity.” Dr. Walter Orr Roberts (NCAR founder) “I have a very strong feeling that science exists to serve human welfare. It’s wonderful to have the opportunity given us by society to do basic research, but in return, we have a very important moral responsibility to apply that research to benefiting humanity.” Walter Orr Roberts