CHG rainfall products Make best possible rainfall products for monitoring crop stress in areas of rain fed agriculture. Objectives:

Slides:

Advertisements

Similar presentations

1/13/2003IRI Presentation E D C F E W S An empirical study of the links between NDVI and atmospheric variables in Africa with applications to forecasting.

Advertisements

Daily Precipitation Statistics: An Intercomparison between NCEP Reanalyses and Observations Vernon Kousky, Wayne Higgins & Viviane Silva 5 August 2011.

CHG Station Climatology Database (CSCD)

Consolidated Seasonal Rainfall Guidance for Africa, November 2013 Initial Conditions Issued 7 November 2013 Forecast maps Forecast Background – ENSO update.

Spatial and Temporal Variability of GPCP Precipitation Estimates By C. F. Ropelewski Summarized from the generous input Provided by G. Huffman, R. Adler,

A Global Daily Gauge-based Precipitation Analysis, Part I: Assessing Objective Techniques Mingyue Chen & CPC Precipitation Working Group CPC/NCEP/NOAA.

Climate Predictability Tool (CPT)

Characteristics of High-Resolution Satellite Precipitation Products in Spring and Summer over China Yan Shen 1, A.-Y. Xiong 1 Pingping Xie 2 1. National.

U.S. Department of the Interior U.S. Geological Survey NASA/USDA Workshop on Evapotranspiration April 6, 2011 – Silver Spring, Maryland ET for famine early.

We are developing a seasonal forecast system for agricultural drought early warning in sub- Saharan Africa and other food insecure locations around the.

CHG rainfall products Make best possible rainfall products for monitoring crop stress in areas of rain fed agriculture. Objectives:

Visit with Anthony Watts April 23, NOAA’s National Climatic Data Center U.S. HCN Temperature Trends: A brief overview.

The Climate Prediction Center Rainfall Estimation Algorithm Version 2 Tim Love -- RSIS/CPC.

Early warning Systems in Sudan Meteorological Authority Ahmed M Abdel Karim Sudan Meteorological Authority Crop and RAngeland Monitoring.

T ropical A pplications of M eteorology using SAT ellite data Ross Maidment TAMSAT Research Group, University of Reading.

CPC Seasonal Forecasts ASO 2005-JAS 2006 Edward O’Lenic Michael Halpert, David Unger NOAA-NWS-CPC 31 st Climate Diagnostics and Prediction Workshop Boulder,

AGU Fall Meeting: Tuesday,

2011/2012 Winter and Spring Temperature and Precipitation Outlook Southern Plains Drought Assessment and Outlook Forum Fort Worth, TX November 29, 2011.

Arctic Land Surface Hydrology: Moving Towards a Synthesis Global Datasets.

Climate Variability and Prediction in the Little Colorado River Basin Matt Switanek 1 1 Department of Hydrology and Water Resources University of Arizona.

Tahiti, Darwin, and pressure oscillations. SOI = Tahiti - Darwin (normalized)

FTIPP FCLIM / TRMM / IRP Precipitation Pentads

Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 Correction of Vegetation Time Series for Long Term Monitoring Marco Vargas¹.

Hurricane Climatology and the Seasonal Forecast for the 2012 Hurricane Season John Cole and Andrew McKaughan, NOAA/NWS WFO Newport/Morehead City, NC.

Potential Predictability of Drought and Pluvial Conditions over the Central United States on Interannual to Decadal Time Scales Siegfried Schubert, Max.

A Kalman Filter Approach to Blend Various Satellite Rainfall Estimates in CMORPH Robert Joyce NOAA/NCEP/CPC Wyle Information Systems Pingping Xie NOAA/NCEP/CPC.

1 GOES-R AWG Hydrology Algorithm Team: Rainfall Probability June 14, 2011 Presented By: Bob Kuligowski NOAA/NESDIS/STAR.

The Evaluation of a Passive Microwave-Based Satellite Rainfall Estimation Algorithm with an IR-Based Algorithm at Short time Scales Robert Joyce RS Information.

John Janowiak Climate Prediction Center/NCEP/NWS Jianyin Liang China Meteorological Agency Pingping Xie Climate Prediction Center/NCEP/NWS Robert Joyce.

CPC Unified Gauge – Satellite Merged Precipitation Analysis for Improved Monitoring and Assessments of Global Climate Pingping Xie, Soo-Hyun Yoo,

PAGASA-DOST Presscon - 04 October 2010 Amihan Conference Room.

Development and evaluation of Passive Microwave SWE retrieval equations for mountainous area Naoki Mizukami.

All about DATASETS Description and Algorithms Description and Algorithms Source Source Spatial and temporal Resolutions Spatial and temporal Resolutions.

Young-Kwon Lim, D.W. Shin, S. Cocke, T. E. LaRow, J. J. O’Brien, and E. P. Chassignet Center for Ocean-Atmospheric Prediction Studies, Florida State University,

Combining CMORPH with Gauge Analysis over

NOAA/Climate Prediction Center Outlooks for Spring-Summer, 2010 Ed O’Lenic Chief, Operations Branch NOAA-NWS-Climate Prediction Center Weatherbug Energy.

Quality control of daily data on example of Central European series of air temperature, relative humidity and precipitation P. Štěpánek (1), P. Zahradníček.

Figure (a-c). Latitude-height distribution of monthly mean ozone flux for the months of (a) January, (b) April and (c) July averaged over years 2000 to.

Regional Verification of CPC’s Seasonal Outlooks Michael Halpert & Kenneth Pelman Climate Prediction Center.

Thomas R. Karl Director, National Climatic Data Center, NOAA Editor, Journal of Climate, Climatic Change & IPCC Climate Monitoring Panel Paul D. Try, Moderator.

L3MTO Transfer to CPC Preliminary Meeting February 7, 2008.

CPC Unified Precipitation Project Pingping Xie, Wei Shi, Mingyue Chen and Sid Katz NOAA’s Climate Prediction Center

A Global Kalman Filtered CMORPH using TRMM to Blend Satellite Rainfall Robert Joyce NOAA/NCEP/CPC Wyle Information Systems Pingping Xie NOAA/NCEP/CPC John.

A Global Rainfall Validation Strategy Wesley Berg, Christian Kummerow, and Tristan L’Ecuyer Colorado State University.

Validation of Satellite-Derived Rainfall Estimates and Numerical Model Forecasts of Precipitation over the US John Janowiak Climate Prediction Center/NCEP/NWS.

The Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) Dataset: Quasi-Global Precipitation Estimates for Drought Monitoring and Trend.

Consolidated Seasonal Rainfall Guidance for Global Tropics, December 2015 Initial Conditions Issued 14 December 2015 Forecast Background – ENSO update.

U.S. Department of the Interior U.S. Geological Survey Evaluating the drought monitoring capabilities of rainfall estimates for Africa Chris Funk Pete.

A new high resolution satellite derived precipitation data set for climate studies Renu Joseph, T. Smith, M. R. P. Sapiano, and R. R. Ferraro Cooperative.

1. Session Goals 2 __________________________________________ FAMINE EARLY WARNING SYSTEMS NETWORK Become familiar with the available data sources for.

Comparison of Precipitation Products for 2014 Ethiopian Growing Season chg.geog.ucsb.edu Pete Peterson 1, Chris C Funk 2,1, Gregory.

1 Recent Reduction in the Area of “EC” in CPC Outlooks and its Relationship to Value Ed O’Lenic, Kenneth Pelman, and David Unger NOAA-NWS-Climate Prediction.

 Each climate graph is made up of 2 major parts. ◦ A line graph for temperature.  Represented by a red line for the high temperatures  Represented.

CLIMATE GRAPHS. Temp In °C Precipitation In mm OTTAWA LABELS! CITY AT TOP TEMPERATURE ON LEFT IN °C PRECIPITATION ON RIGHT MONTHS ACROSS THE BOTTOM.

Trend Analysis for Central America Diego Pedreros Greg Husak Oct, 2009.

Using FAO NewClim to Build Climatologies Primary attempt – May a. verdin 08/20/2010.

A global, 2-hourly atmospheric precipitable water dataset from ground-based GPS measurements and its scientific applications Junhong (June) Wang NCAR/EOL/T.

1. Session Goals 2 __________________________________________ FAMINE EARLY WARNING SYSTEMS NETWORK Understand use of the terms climatology and variability.

Oct 2012 The FEWS NET’s Rainfall Enhancement Process Chris Funk 1, Pete Peterson 2, Marty Landsfeld 2, Andrew Verdin, Diego Pedreros 1, Joel Michaelsen.

Phil Arkin, ESSIC University of Maryland With thanks to: Pingping Xie, John Janowiak, and Bob Joyce Climate Prediction Center/NOAA Describing the Diurnal.

1 Assessment of the CFSv2 real-time seasonal forecasts for 2014 Wanqiu Wang, Mingyue Chen, and Arun Kumar CPC/NCEP/NOAA.

High Resolution Gauge – Satellite Merged Analyses of Precipitation: A 15-Year Record Pingping Xie, Soo-Hyun Yoo, Robert Joyce, Yelena Yarosh, Shaorong.

Man-sze, CHEUNG Hong Kong Observatory

The El Niño and La Niña Effects on the Hydrology of Texas

Rain Gauge Data Merged with CMORPH* Yields: RMORPH

TAMSAT African Rainfall Climatology And Time-series

Validation of Satellite Precipitation Estimates using High-Resolution Surface Rainfall Observations in West Africa Paul A. Kucera and Andrew J. Newman.

An Inter-comparison of 5 HRPPs with 3-Hourly Gauge Estimates

Evaluation of Gridded Snow Products Using CloudSat Snowfall Estimates

Facultad de Ingeniería, Centro de Cálculo

Presentation transcript:

CHG rainfall products Make best possible rainfall products for monitoring crop stress in areas of rain fed agriculture. Objectives:

CHG rainfall products Make best possible rainfall products for monitoring crop stress in areas of rain fed agriculture. Results: Objectives: CHIRP ( present) and FTIP ( present) Global unbiased 0.05 degree pentads available with 2 day lag tinyurl.com/chg-products

CHG rainfall products Make best possible rainfall products for monitoring crop stress in areas of rain fed agriculture. Improved Results: Objectives: CHIRP/FTIP blended with station data, 1 pentad lag Results: CHIRP ( present) and FTIP ( present) Global unbiased 0.05 degree pentads available with 2 day lag tinyurl.com/chg-products

Quick overview of how we get there… Start with long term monthly average rainfall maps FCLIM Aside: build monthly models of rainfall trained on TRMM-V7 –derive coefficients a and b, such that rain rate = a * CCD + b (IRP) –where CCD is the percentage of observation where IR temp < threshold Calculate IRP pentads from CPC IR data ( present) and B1 IR data ( ) CHIRP (1981-present) = FCLIM * (IRP %normal) Use TRMM-RT7 data ( present) FTIP (2000-present) = FCLIM * (IRP %normal + TRMM-RT7 %normal)/2 SPACE TIME

CHIRP º, global, unbiased, rainfall pentad [mm] with 2 day lag.

FTIP º, global, unbiased, rainfall pentad [mm] with 2 day lag.

FCLIM derived wet season JFM FMA MAM AMJ MJJ JJA JAS ASO SON OND NDJ DJF

Validation analysis for each data set to validate, CHIRP, FTIP, TRMM, ECMWF… build cubes of 2 month totals based on growing season. Season time series (2001 – 2011) JFM [J1+F1, J1+M1, F1+M1, …, J11+F11, J11+M11, F11+M11]... JAS [J1+A1, J1+S1, A1+S1, …, J11+A11, J11+S11, A11+S11]... NDJ [N1+D1, N1+J2, D1+J2, …, N11+D11, N11+nan, D11+nan] longitude latitude time series longitude latitude time series ‘True’ FTIP correlation bias ratio mean absolute error misses

Correlation with interpolated stations TRMM-RT7 ECMWF CHIRP FTIP

Bias ratio to interpolated stations >1.5 TRMM-RT7 ECMWF CHIRP FTIP

Mean Absolute Error with interpolated stations >100 TRMM-RT7 ECMWF CHIRP FTIP

Misses compared to interpolated stations >9 TRMM-RT7 ECMWF CHIRP FTIP

CHG Station Climate Database Daily rainfall observations from many public and private sources going back to the 1800’s Currently ~950,000 records in db Used to create spatially interpolated fields Challenge identifying duplicates, non-zero zeroes Constantly topping off db from available sources Blended into satellite rainfall products

January 2009 Original

January 2009 Fixed

B1 fails 2

B1 fails 3

Summary 2 rainfall products, CHIRP (1981-present) & FTIP (2000-present) Global, 0.05 º, unbiased, pentads with 2 day lag. Compare very well with existing rainfall products. Developing an extensive station rainfall database Used to blend station data with CHIRP and FTIP

Thank You … Chris Funk Greg Husak Joel Michaelsen Diego Pedreros Andrew Verdin Marty Landsfeld Boleslo Romero FEWS NET Data available via anonymous ftp: tinyurl.com/chg-products

CHG Rainfall Product Paths FCLIM CHG Stations database FTIPS 2005-prst, 5Km, 5days CCD model IRP prnt BIRP Join 80-prst CHIRP CHIRPS 1980-present, 5Km, 5days FTIP FCLIMS, 1920 present, 5Km, 5days Source Datasets Station Average monthly rainfall Rainfall predictors TRMM7-RT 2000-prsnt IR-B CPC-IR – 2000-prsnt TRMMV present

Some data issues. B1 glitches Station zeroes not zero

IDW weights for nearest 3 neighbors mapped to red, green, blue

Geostarionary coverage

How to make FAT clims pentad, pixel, p-3 p-2 p-1 p p+1 p+2 p+3 = 2176 points/year CHIRP clim 09.6

FCLIM derived wet season JFM FMA MAM AMJ MJJ JJA JAS ASO SON OND NDJ DJF

Validation - Africa correlations CMORPH CPC-Unified PERSIANN TRMM-V6 TRMM-V7 TRMM-RT7 CHIRP FTIP-V7 FTIP-RT7

Validation - Correlations red CMORPH ECMWF TRMM-RT7 FTIP-RT

Validation - Correlations 2 red PERSIANN TRMM-V6 TRMM-V7 CHIRP-V7

Validation - Correlations 2 blue PERSIANN TRMM-V6 TRMM-V7 CHIRP-V

Validation - Mean Absolute Error >100 PERSIANN TRMM-V6 TRMM-V7 CHIRP-V7

Validation - Bias ratio >1.5 PERSIANN TRMM-V6 TRMM-V7 CHIRP-V7

Validation - Misses 2 PERSIANN TRMM-V6 TRMM-V7 CHIRP-V >9

Correlation with interpolated stations TRMM-RT7 ECMWF CHIRP FTIP

Bias ratio to interpolated stations >1.5 TRMM-RT7 ECMWF CHIRP FTIP

Mean Absolute Error with interpolated stations >100 TRMM-RT7 ECMWF CHIRP FTIP

Misses compared to interpolated stations >9 TRMM-RT7 ECMWF CHIRP FTIP

B1 fails

B1 fails 4

CHIRP º, global, unbiased, rainfall pentad [mm] with 2 day lag.

FTIP º, global, unbiased, rainfall pentad [mm] with 2 day lag.