umi contributions to wp2 - climax-sa.org€¦ · sis web implementation cfsv2 weekly forecast...
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![Page 1: UMI Contributions to WP2 - climax-sa.org€¦ · SIS web implementation CFSv2 weekly forecast anomalies visualization (pre, t2m, z200,olr) Monitoring variables visualization ( tmax,tmin,tmed,pre](https://reader034.vdocuments.site/reader034/viewer/2022050501/5f938649ee080f351f360e49/html5/thumbnails/1.jpg)
CIMA-UMI Contributions to WP2
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Motivation: Climate variability and Change in southeastern South
America
(gris) Anomalías de lluvia de DEF en el sudeste de Sudamérica y su correspondiente (rojo) variabilidad decadal, (verde, azul variabilidad
multidecadal y (línea negra) tendencia lineal
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CMIP5 Model skill in representing EOF1
Predictability of decadal climate variability in South
America
Díaz Vera, and Saurral (2016) 3
(Assessment of CMIP5 decadal predictions is in progress)
GFDL-CM3 Model historical run
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Regional predictability on seasonal timescales (Marisol Osman)
•Assessment of the predictability and skill of climate anomalies over South America considering a multi-model ensemble of 99 seasonal forecasts from 9 coupled global circulation models included in the Climate Historical Forecast Project (CHFP)/WCRP.
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Goal
• To apply the Ensemble Regression Technique to precipitation model outputs from WCRP/CHFP project over South America.
• To assess the performance over tropical and extratropical regions against the direct multi-model output
Marisol Osman CPC Climate Diagnostics and Prediction Workshop
Calibrated probabilistic climate predictions on seasonal timescales (Marisol Osman)
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Data
• 11 models participating in the Climate Historical Forecast Project (CHFP). ~ 10 ensemble members each
• Precipitation forecast valid at December-January-February and June-July-August for the 1982-2006 period, made with IC from Nov and May, respectively (Lead 1 month).
• CMAP precip used as observations
JJA DJF
Marisol Osman CPC Climate Diagnostics and Prediction Workshop
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Methods
For each model:
• Detrended and Standardized ensemble
• We applied Ensemble Regression PDF that represents each ensemble set
• We evaluated the PDF of each model at the observation Determine the probability of each model of being the best Model’s Weight
• One-year-out cross-validation was used
Marisol Osman CPC Climate Diagnostics and Prediction Workshop
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Consolidation
Marisol Osman CPC Climate Diagnostics and Prediction Workshop
Two ways to consolidate models
WKERNELS: Sum Weighted PDF to get a consolidated PDF
WSEREG: Apply EREG to the
Weighted Super-Ensemble
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DJF ACC
Simple MME WKERNELS WSEREG
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DJF Reliab. and ROC diagrams - Tropics
Simple MME WKERNELS WSEREG
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DJF Reliab. and ROC diagrams – East of Extratrop. Andes
Simple MME WKERNELS WSEREG
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Web-based tools of climate monitoring and prediction (Alfredo Rolla)
SIS web implementation CFSv2 weekly forecast anomalies visualization (pre, t2m, z200,olr)
Monitoring variables visualization ( tmax,tmin,tmed,pre ) Visualization Libraries (building block) :
Openlayers (mapping) D3js (Data Driven Documents) D3jsgeo (Geographic projections ext.)
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Calibrated probabilistic climate predictions on subseasonal timescales
(Alfredo Rolla)
Member 1
Member 2
Member 3
Member
CFSV2 n:16 (4 weeks) GEFSv2 n:11 (2 wekks)
Weekly Climate Forecasts
Calibrated regional statitical models of logistic regression
P[ O > Tr | F ]
CFSv2 40 models / region
GEFV2 20 models / region
𝐹𝑒𝑛𝑠4
P[ O > Tr | F ]: Probability that observation in the region exceeds the threshold of 10 mm given the forecast
Expected result
Where: O: Observations Tr: Threshold F: Forecast
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Climate information on subseasonal timescales: Monitoring and prediction (Mariano Alvarez)
SIS pattern: EOF1 of IS-filtered OLR’ (10-90 days)
SIS index = PC1. Describes activity of SIS pattern
if positive, pp favored in green area
if negative, pp inhibited in green area
SIS index monitoring Uses quasi-RT OLR obtained from NOAA. With A. Rolla
Current effort in improving the RT intraseasonal method of filtering to avoid changes in the values of the index
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Climate information on subseasonal timescales: Monitoring and prediction (Mariano Alvarez)
SIS pattern: EOF1 of IS-filtered OLR’ (10-90 days)
Well represented by the CFSv2 model, which runs operationally
SIS index predictability is to be assessed using the 45-day runs of the CFSv2, from week 1 to week 4
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Evolution of the SIS index
during December-January-February 2016