1. High-resolution forecasts of seasonal precipitation: a combined statistical- dynamical downscaling approach Dorita Rostkier-Edelstein 1, Yubao Liu 2, Wanli Wu 2, Pavel Kunin 4, Ming Ge 2, Gael Descombes 2, Amir Givati 3 1Israel Institute for Biological Research, Israel 2National Center for Atmospheric Research, USA 3Israel Hydrological Service, Israel 4 Life Science Research Institute

2. Motivation The need: Predicting the seasonal precipitation for estimating the amount of water expected to flow into the Sea of Galilee and the aquifers during winter time. Available tool: Only coarse global forecasts are available, e.g., CFS-ensemble seasonal forecasts:

3. Motivation, cont’d Challenges: The areas of interest are characterized by complex topography and land-uses not resolved by global models, that lead to complex spatial distribution of precipitation:

4. Motivation, cont’d Most of the precipitation is due to extra-tropical cyclones that interact with the complex terrain leading to a complex spatial distribution. Amounts and spatial distributions change from year to year according to the cyclones frequency.

5. Strategy: Statistical-dynamical downscaling of coarse seasonal forecasts

6. hindcasts Coarse resolution archived hindcasts Predictors vector, X hind, e.g.: Precipitation rate Precipitable water SLP 500-hPa geopotential height 2-m temperature 10-m winds SST Archived Precipitation gauge Observations (or high-resolution calculated precipitation climatography) paired Covariance matrix C=X T hind X hind forecasts Coarse resolution real-time forecasts Predictors vector, X fcst Mahalanobis distances Retain K neighbors and weight Resample observations or high-resolution calculated climatography Predicted precipitation at observations locations (or at high-resolution grid) Use model climatography instead of archived gauge observations if these are sparse in space and time KNN algorithm

7. High-resolution climatography by dynamical downscaling of global re-analyses Global re-analysis Conventional and non-conventional observations High resolution terrain and surface properties Large scale forcing and lateral boundaries WRF-FDDA High-resolution forcing 4D-data assimilation High-resolution calculated precipitation climatography Strategy, cont’d

8. Validation of the KNN-downscaling Skill of KNN-downscaling is dictated by: – Skill of coarse model (does the coarse model correctly reproduce the large-scale circulation, the distribution of weather regimes?). – Reliability of observations dataset. – Suitable choice of large-scale predictors (well correlated to stations precipitation?). – Weighting of the KNN members (do better members contribute with higher weights?).

9. Validation of the KNN-downscaling, cont’d Estimate skill of KNN-downscaling assuming “perfect large- scale flow”: Test KNN algorithm using a very reliable coarse dataset : NCEP/NCAR Reanalysis Project (NNRP). Large-scale predictors optimization: Calculation of maximum correlations between NNRP variables and 18 selected-stations daily precipitations. PREDICTORMAX CCLATITUDE (N) LONGITUDE (E) LEVEL (hPa) MSLP-0.417835.0 T-0.397232.530.0700 Z-0.481735.0 700 RH0.441132.535.0700 OMEGA-0.426332.535.0850 U0.527930.032.51000 V-0.421237.527.51000

10. KNN-algorithm specifications Temporal window of 14 days. 9 grid points surrounding Levant. 15 CFS-ensemble members. Total potential neighbors L = 434 Cutoff, K = 20 nearest neighbors (patterns) are used for downscaling prediction (weights according to their KNN ranks).

11. KNN downscaling of NNRP Validation for February 2009: STATION KNN forecast (mm) Observation (mm) Climate (mm) HAIFA (1)12716294 ELON (2)212213150 GINOSAR (3)13318484 JERUSALEM (4)13820093 KEFAR GILADI (5)241262164 HAR KENAAN (6)175217132 YAD HANNA (7)242202129 HAFEZ HAYYIM (8)12314383 EN HA HORESH (9)158193106 Good reproduction of inter-station variability. Correct tendency with respect to climatologic value. Some underestimations due to missing observations in the database, will be corrected. KNN OBS CLM Stations

12. KNN downscaling of CFS forecasts Good reproduction of inter-station variability. KNN-mean values too close to observed climatology. But individual KNN predictions fit better the observations (KNN-range). How do we improve the resolution w.r.t climatology? Better weighting. Elon Meron Haifa Knaan Ginosar Giladi Hanna Jerus. Horesh Hefetz OBS KNN-range CLM KNN-mean January 2010

13. On-going: Fine tuning of CFS KNN-downscaling Improve choice of predictors and weighting schemes. Evaluate CFS large-scale skill (compare to NNRP): – Does CFS reproduce the true distribution of weather regimes (e.g. Alpert et al., 2004)? – Use CFS ensemble-members with best large-scale skill? Implement a combined CFS-NNRP KNN and statistical forecast strategy.

14. Evaluation of CFS large-scale skill Distribution of weather classes for December months (1981-2008) (classes as defined by Alpert et al., 2004) NNRP Number of events (monthly normalized) 2 CFS-ensemble members Number of events (monthly normalized) Red-Sea troughs Highs Lows (Thanks to Dr. Osetinsky and Prof. Alpert for providing the reference dataset) 11Low to the East (Deep) 12Low to the South (Deep) 13Low to the South (Shallow) 14Low to the North (Deep) 15Low to the North (Shallow) 16cold Low to the West 17Low to the East (Shallow)

15. WRF-precipitation climatography verification: seasonal-accumulated precipitation Gauges and topography Mean seasonal-accumulated precipitation (all seasons in 2005-2009) (mm)

16. WRF-precipitation climatography verification: seasonal-accumulated precipitation 2005-2006 2006-2007 2007-2008 2008-2009 (mm)

17. WRF-precipitation climatography verification: seasonal-accumulated precipitation Good reproduction of spatial and inter-seasonal variability. Further fine tuning and bias-correction going-on.