ON IMPROVING ENSEMBLE FORECASTING OF EXTREME PRECIPITATION USING THE NWS METEOROLOGICAL ENSEMBLE FORECAST PROCESSOR (MEFP) Dong-Jun Seo 1, James Brown 2, Theresa Modrick 3, Konstantine Georgakakos 3, Sunghee Kim 1, Hossein Sadeghi 1, Kevin He 4, Brett Whitin 5, Art Henkel 5 and Rob Hartman 5 1 Dept. of Civil Eng., The Univ. of Texas at Arlington, Arlington, TX, USA 2 Hydrologic Solutions Limited, Southampton, UK 3 Hydrologic Research Center, San Diego, CA, USA 4 California Dept. of Water Resources, Sacramento, CA, USA 5 California-Nevada River Forecast Center, NOAA/NWS, Sacramento, CA, USA This material is based in part upon work supported by NWS Contract DG-133W-13-CQ- 0042 and by the Sectoral Applications Research Program (SARP) of the NOAA Climate Program Office (CPO) Grant NA15OAR4310109. 1
Hydrologic Ensemble Forecast Service(HEFS) Demargne et al. (2014) 2
MEFP System Short- Range WPC/RFC forecasts Ensembles (days 1-5) Medium- Range Long- Range GEFS ens. mean fcsts CFSv2 forecasts Ensembles (days 1-15) Ensembles (out to 9 months) Merging Calibrated short- to long-range forcing ensembles Climatology Ensembles (out to one year) 3
MEFP Methodology (Schaake et al. 2007, Wu et al. 2011) Historical observation Modeling of bivariate distribution Historical single-valued forecast Fcst precip Obs precip MEFP Parameters Real-time single-valued forecast Sampling from conditional distribution Obs precip Fcst precip Schaake Shuffle (Clark et al. 2004) Ensemble forecasts 4
21 10 20 Modulation 1 7 14 19 4 9 12 16 18 1 2 3 5 6 8 11 13 15 17 Base 2 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 Base 1 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 lead time (hour) 6 12 18 24 30 36 42 48 54 60 66 72 78 84 90 96 102 108 114 120 126 132 138 144 150 156 162 168 174 180 186 192 198 204 210 216 222 228 234 240 246 252 258 264 270 276 282 288 294 300 306 312 318 324 330 336 342 348 354 360 Canonical events (scales) Improving ensemble forecasting of extreme precipitation using MEFP 5
Improving ensemble forecasting of From extreme precipitation He et al. using (2015) MEFP 6
Data extension using surrogate basins Motivation Improve the forecasts of large reservoir inflows in California for reservoir management. Improve the quality of the MEFP precipitation forecasts for the few large and extreme amounts. Approach Utilize data from a number of appropriately chosen surrogate basins to enhance the historical database used to produce MEFP ensemble precipitation forecasts for a target basin. Emphasize 3-day precipitation amounts relevant to reservoir management Demonstrate approach and verify results for 4 sub-basins of the Folsom Lake drainage Dec 18, 2015 AGU Meeting, San Francisco, CA 7
Selection of Surrogate Basins Broad similarity criteria (precipitation forcing and flow response) Drainage area (+/- 20%) Mean areal aspect (180-360 degrees from North) Mean areal slope ( > MAS of target basin) Climatological rain/snow volume over a year (< 1 or > 1) Mutual independence of top 3% of extreme MAP events Closeness of CDFs of the surrogate basins to that of Target basin (MEFP) Example NFDC1HUF Dec 18, 2015 AGU Meeting, San Francisco, CA 8
Study basins Four target (sub-)basins 1. AKYC1HUF 2. CBAC1LLF 3. NFDC1HLF 4. NFDC1HUF All contribute to Folsom Lake inflow Multiple surrogate basins identified Surrogates based on a combination of physical and statistical similarity to target basin 9
Validation strategy Focus of validation Large and extreme precipitation amounts (MAP) Relevant to Folsom Lake inflows (3-day accumulation) Multiple attributes of quality, including skill vs. baseline Dependent and independent validation Target basin used for calibration in both cases Independent: forecasting a pseudo-target basin Pseudo-target chosen for similarity to target and verifying observations CDF-matched to target Dec 18, 2015 AGU Meeting, San Francisco, CA 10
Dependent validation POR = Period of Record Validation POR for Surrogate 2 POR for Surrogate 1 POR for Target Baseline calibration Extended calibration GEFS reforecast dataset used throughout 11
Location adjustment Example for TMDC1HUF Plots show 3-day MAP (x-axis) versus GEFS (y-axis) One plot for each of four grid cells closest to TMDC1HUF 12
Homogenize GEFS ensemble mean hindcasts for surrogate basins C2: Multiplicative bias correction (w/ loc. adj.) Consider only the upper tail C3: CDF matching (w/o loc. adj.) Transform GEFS ensemble mean hindcasts for surrogate basins to CDF-match with those for the target basin homogenizes marginal statistics but robs natural variability particularly in the upper tail Do the same for observed MAP 13
Dependent validation results Modest gains in skill Results shown for three target sub-basins Residual skill (BSS) from extended calibrations (C2, C3) Some improvements at high thresholds but they are modest at best Larger improvements at NFDC1HUF (loc. adj.) Ignoring NFDC1HUF, these improvements come at expense of moderate thresholds Modest gains Hurts moderate thresholds Mainly location adjustment 14
CRPSS More recent findings: Adding modulation events improves skill for heavy precipitation CRPSS of MEFP ensembles based on both base and modulation events relative to those based only on base events Up to 35% improvement for high thresholds Upper Trinity River Basin in North Texas From Sadeghi (2015) 15
Likely source of conditional bias Note that: Location adjustment increases correlation Modulation events have larger time scales of aggregation than base events, and hence are likely to produce larger correlation (and applied last) This has the effect of increasing ρ and hence reducing conditional bias (but at the expense of compromising spread) Currently, MEFP uses Method 2 in Wu et al. (2011) z o z f E[ P o P f p f ] NQT 1 o ( z o ) f ( z o z f ) dz o where f ( z o z f ) N( z f, 2 ) Dec 18, 2015 AGU Meeting, San Francisco, CA 16
Increased correlation to 0.98 for all GEFS canonical events From He et al. (2015) 17
Possible (quick) remedy? From Kim (2015) J Raw precip. vs. truth Method 2 vs. truth Conditional biaspenalized optimal * 2 * 2 estimate vs. truth CBPK E Z * 0, Z 0 [( Z 0 Z 0 ) ] E Z 0 [{ E Z * 0 ] Z Brown and Seo (2011) Conditional bias-penalized indicator cokriging Seo (2013) Conditional bias-penalized optimal linear estimation, conditional bias-penalized kriging Seo et al. (2014) Extended conditional bias-penalized kriging 18 [ Z 0 Z o o } ]
Conclusions and recommendations Examine conditional probabilistic approaches to better represent the conditions associated with large and extreme events Allow conditional parameter optimization under userdefined criteria to generate parameters that are specific to extreme precipitation events Consider implementing conditional bias-penalized optimal estimation, possibly, in combination with Method 3 (Wu et al. 2011) Improve the statistical modeling within the MEFP by enhancing the set of available marginal distributions for the non-zero precipitation data Dec 18, 2015 AGU Meeting, San Francisco, CA 19
Conclusions and recommendations (cont.) Improving ensemble forecasting of extreme precipitation using MEFP Support a development version of the MEFP and an ensemble sandbox that would allow for fast prototyping, testing, and evaluation of new techniques and enhancements Data extension following the above (and other) improvements The use of predictors in addition to the ensemble mean for their skill in predicting large and extreme events Dynamic downscaling of GEFS to preserve skill in raw forcing to the MEFP and account for the influence of terrain and orographic enhancement of large and extreme events Dec 18, 2015 AGU Meeting, San Francisco, CA 20