STATISTICAL DOWNSCALING OF DAILY PRECIPITATION IN THE ARGENTINE PAMPAS REGION

Transcription

1 STATISTICAL DOWNSCALING OF DAILY PRECIPITATION IN THE ARGENTINE PAMPAS REGION Bettolli ML- Penalba OC Department of Atmospheric and Ocean Sciences, University of Buenos Aires, Argentina National Council of Research and Development (CONICET), Argentina ICRC-CORDEX 2016

2 MOTIVATION The Pampas covers the most productive agricultural land in Argentina The main crops are: Soybean, corn, wheat and sunflower Pampas Region

3 MOTIVATION Argentina is the largest exporter of soybean products and in terms of production size comes third in the world after the United States and Brazil Pampas Region

4 MOTIVATION Crops grow under rain-fed conditions. Precipitation is one of the principal climatic variables of influence for the production and management of crops Pampas Region

5 MOTIVATION CORDEX-ESD-Experiment 1 was set and performed in the La Plata Basin region. Prior to this experiment, empirical statistical downscaling on a daily basis had not been widely developed or applied over the region La Plata Basin Pampas Region South America

6 MOTIVATION There is a need to promote ESD applications in southern South American regions La Plata Basin Pampas Region South America

7 OBJECTIVE To calibrate and validate a statistical method to downscale daily precipitation in the Argentine Pampas region.

8 DATA AND METHODOLOGY 0 Predictand: Daily precipitation. 28 weather stations. Predictors: NCEP R2 daily fields. Three different domains (orange rectangles) MSLP hgt 500 hpa hgt 850 hpa u 850 hpa v 850 hpa v 200 hpa T 850 hpa rhum 850 hpa -60

9 DATA AND METHODOLOGY Analogue Method: Unrestricted analogues were chosen in a different calendar + year - 1 year Euclidean distance Different domains and combinations of variables were evaluated Calibration: Validation:

10 DATA AND METHODOLOGY Different aspects of daily precipitation were evaluated through different metrics: Precipitation ocurrence Precipitation amount

11 Occurrence Index I: (Timbal et al. 2003) Assesses false alarms, missed forecasts and hits of rainy days The higher the index, the better the method performance

12 45 Occurrence 40 Index I MSLPLD hgt500ld u850ld v850ld u850ld_v850ld T850LD rhum850ld hgt500ld_t850ld MSLPLD_T850LD hgt500ld_rhum850ld u850ld_v850ld_t850ld hgt500md MSLPMD u850md v850md T850MD rhum850md hgt500md_t850md Combinations of Predictor Variables and Domains Median 25%-75% Min-Max hgt500md_rhum850md MSLPMD_T850MD MSLPMD_rhum850MD u850md_v850md_t850md u850md_v850md_rhum850md hgt500ld_t850md hgt500ld_rhum850md MSLPLD_T850MD MSLPSD_T850SD hgt500sd_t850sd u850sd_v850sd_t850sd u850sd_v850sd_rhum850sd u850sd_v850sd_rhum850sd_t850sd MSLPSD_T850SD_rhum850SD MSLPMD_T850SD_rhum850SD u850md_v850md_t850sd_rhum850sd

13 Each group of 5 boxplots correspond to Summer, Fall, Winter, Spring and Annual Index I. 45 Occurrence 40 Index I Boxplots of the index I values between daily estimated and observed values over the 28 meteorological stations MSLPLD hgt500ld u850ld v850ld u850ld_v850ld Median 25%-75% Min-Max T850LD rhum850ld hgt500ld_t850ld MSLPLD_T850LD hgt500ld_rhum850ld u850ld_v850ld_t850ld hgt500md MSLPMD u850md v850md T850MD rhum850md hgt500md_t850md Combinations of Predictor Variables and Domains hgt500md_rhum850md MSLPMD_T850MD MSLPMD_rhum850MD u850md_v850md_t850md u850md_v850md_rhum850md hgt500ld_t850md hgt500ld_rhum850md MSLPLD_T850MD MSLPSD_T850SD hgt500sd_t850sd u850sd_v850sd_t850sd u850sd_v850sd_rhum850sd u850sd_v850sd_rhum850sd_t850sd MSLPSD_T850SD_rhum850SD MSLPMD_T850SD_rhum850SD u850md_v850md_t850sd_rhum850sd

14 Each group of 5 boxplots correspond to Summer, Fall, Winter, Spring and Annual Index I. 45 Occurrence 40 Index I Boxplots of the index I values between daily estimated and observed values over the 28 meteorological stations MSLPLD hgt500ld u850ld v850ld u850ld_v850ld Median 25%-75% Min-Max T850LD rhum850ld hgt500ld_t850ld MSLPLD_T850LD hgt500ld_rhum850ld u850ld_v850ld_t850ld hgt500md MSLPMD u850md v850md T850MD rhum850md hgt500md_t850md Combinations of Predictor Variables and Domains hgt500md_rhum850md MSLPMD_T850MD MSLPMD_rhum850MD u850md_v850md_t850md u850md_v850md_rhum850md hgt500ld_t850md hgt500ld_rhum850md MSLPLD_T850MD MSLPSD_T850SD hgt500sd_t850sd u850sd_v850sd_t850sd u850sd_v850sd_rhum850sd u850sd_v850sd_rhum850sd_t850sd MSLPSD_T850SD_rhum850SD MSLPMD_T850SD_rhum850SD u850md_v850md_t850sd_rhum850sd

15 Each group of 5 boxplots correspond to Summer, Fall, Winter, Spring and Annual Index I. 45 Occurrence 40 Index I Boxplots of the index I values between daily estimated and observed values over the 28 meteorological stations The skill is improved when zonal and meridional wind components are considered east of the Andes range: The Andes range canalizes the moisture advection to the region of interest. Median The reanalyisis performance 25%-75% near the Andes range is affected particularly at low levels. Min-Max (Timbal 2004) MSLPLD hgt500ld u850ld v850ld u850ld_v850ld T850LD rhum850ld hgt500ld_t850ld MSLPLD_T850LD hgt500ld_rhum850ld u850ld_v850ld_t850ld hgt500md MSLPMD u850md v850md T850MD rhum850md hgt500md_t850md hgt500md_rhum850md MSLPMD_T850MD MSLPMD_rhum850MD u850md_v850md_t850md u850md_v850md_rhum850md hgt500ld_t850md hgt500ld_rhum850md MSLPLD_T850MD MSLPSD_T850SD hgt500sd_t850sd u850sd_v850sd_t850sd u850sd_v850sd_rhum850sd u850sd_v850sd_rhum850sd_t850sd MSLPSD_T850SD_rhum850SD MSLPMD_T850SD_rhum850SD u850md_v850md_t850sd_rhum850sd

16 Each group of 5 boxplots correspond to Summer, Fall, Winter, Spring and Annual Index I. 45 Occurrence 40 Index I Boxplots of the index I values between daily estimated and observed values over the 28 meteorological stations In all cases: the highest Index I values are found in Winter and the lowest in Summer (Timbal 2004) MSLPLD hgt500ld u850ld v850ld u850ld_v850ld Median 25%-75% Min-Max T850LD rhum850ld hgt500ld_t850ld MSLPLD_T850LD hgt500ld_rhum850ld u850ld_v850ld_t850ld hgt500md MSLPMD u850md v850md T850MD rhum850md hgt500md_t850md hgt500md_rhum850md MSLPMD_T850MD MSLPMD_rhum850MD u850md_v850md_t850md u850md_v850md_rhum850md hgt500ld_t850md hgt500ld_rhum850md MSLPLD_T850MD MSLPSD_T850SD hgt500sd_t850sd u850sd_v850sd_t850sd u850sd_v850sd_rhum850sd u850sd_v850sd_rhum850sd_t850sd MSLPSD_T850SD_rhum850SD MSLPMD_T850SD_rhum850SD u850md_v850md_t850sd_rhum850sd

17 Each group of 5 boxplots correspond to Summer, Fall, Winter, Spring and Annual RMSE. Amount RMSE Boxplots of RMSE values between daily estimated and observed values over the 28 meteorological stations MSLPLD hgt500ld u850ld v850ld u850ld_v850ld T850LD rhum850ld hgt500ld_t850ld MSLPLD_T850LD hgt500ld_rhum850ld u850ld_v850ld_t850ld hgt500md MSLPMD u850md v850md T850MD rhum850md hgt500md_t850md Combinations of Predictor Variables and Domains Median 25%-75% Min-Max hgt500md_rhum850md MSLPMD_T850MD MSLPMD_rhum850MD u850md_v850md_t850md u850md_v850md_rhum850md hgt500ld_t850md hgt500ld_rhum850md MSLPLD_T850MD MSLPSD_T850SD hgt500sd_t850sd u850sd_v850sd_t850sd u850sd_v850sd_rhum850sd u850sd_v850sd_rhum850sd_t850sd MSLPSD_T850SD_rhum850SD MSLPMD_T850SD_rhum850SD u850md_v850md_t850sd_rhum850sd

18 Each group of 5 boxplots correspond to Summer, Fall, Winter, Spring and Annual RMSE. Amount RMSE Boxplots of RMSE values between daily estimated and observed values over the 28 meteorological stations RMSE decrease when zonal and meridional wind components are considered Combinations of Predictor Variables and Domains east of the Andes range. The lowest RMSE values are found in Winter MSLPLD hgt500ld u850ld v850ld u850ld_v850ld Median 25%-75% Min-Max T850LD rhum850ld hgt500ld_t850ld MSLPLD_T850LD hgt500ld_rhum850ld u850ld_v850ld_t850ld hgt500md MSLPMD u850md v850md T850MD rhum850md hgt500md_t850md hgt500md_rhum850md MSLPMD_T850MD MSLPMD_rhum850MD u850md_v850md_t850md u850md_v850md_rhum850md hgt500ld_t850md hgt500ld_rhum850md MSLPLD_T850MD MSLPSD_T850SD hgt500sd_t850sd u850sd_v850sd_t850sd u850sd_v850sd_rhum850sd u850sd_v850sd_rhum850sd_t850sd MSLPSD_T850SD_rhum850SD MSLPMD_T850SD_rhum850SD u850md_v850md_t850sd_rhum850sd

19 Combination: u + v + rhum + T 850 hpa Small Domain Validation Period:

20 Interannual variability Summer precipitation frequency Correlation values

21 Interannual variability Summer precipitation frequency Correlation values Good general agreement in the validation period

22 Interannual variability Summer precipitation amount Correlation values

23 Interannual variability Summer precipitation amount Correlation values The agreement depends on the station

24 Wet spells: Winter Summer

25 Wet spells: Winter Summer The probability of wet spells is well captured except for Santa Rosa St in Winter (low precipitation in Winter)

26 Distributions: daily precipiation amount Winter K-S Test, no difference 95% confidence Concordia St Observed Concordia St Estimated Summer Concodia St Observed Concordia St Estimated No of obs 300 No of obs Daily Precipitation (mm) Santa Rosa St Observed Santa Rosa St Estimated Daily Precipitation (mm) Santa Rosa St Observed Santa Rosa St Estimated No of obs No of obs

27 Cold Season Warm Season RESULTS 75th Percentile Observed Estimated AN NCEP R mm/day

28 Cold Season Warm Season RESULTS 75th Percentile Observed Estimated AN NCEP R2 Good agreement in the spatial distribution and values of the 75th underestimates the percentile in both seasons. 75th Percentile NCEP R mm/day

29 Concluding Remarks The ESD performance depends on the season and station: the lowest skill was found in Summer related to small scale processes that lead to precipitation. However, the results show the great potential of the method which is able to reproduce different aspects of daily precipitation with a very good level of accuracy.

30 Concluding Remarks These results encurage us to: continue exploring the advantages of ESD to produce actionable regional climate information evaluate different ESD methods apart from the analogue method

31 Thanks!