ABSTRACT
CHIRPS-GEFS is an operational data set that provides daily bias-corrected forecasts for next 1-day to ~15-day precipitation totals and anomalies at a quasi-global 50-deg N to 50-deg S extent and 0.05-degree resolution. These are based on National Centers for Environmental Prediction (NCEP) Global Ensemble Forecast System version 12 (GEFS v12) precipitation forecasts. CHIRPS-GEFS forecasts are compatible with Climate Hazards center InfraRed Precipitation with Stations (CHIRPS) data, which is actively used for drought monitoring, early warning, and near real-time impact assessments. A rank-based quantile matching procedure is used to transform GEFS v12 "reforecast" and "real-time" forecast ensemble means to CHIRPS spatial-temporal characteristics. Matching distributions to CHIRPS makes forecasts better reflect local climatology at finer spatial resolution and reduces moderate-to-large forecast errors. As shown in this study, having a CHIRPS-compatible version of the latest generation of NCEP GEFS forecasts enables rapid assessment of current forecasts and local historical context. CHIRPS-GEFS effectively bridges the gap between observations and weather predictions, increasing the value of both by connecting monitoring resources (CHIRPS) with interoperable forecasts.
ABSTRACT
We present a high-resolution daily temperature data set, CHIRTS-daily, which is derived by merging the monthly Climate Hazards center InfraRed Temperature with Stations climate record with daily temperatures from version 5 of the European Centre for Medium-Range Weather Forecasts Re-Analysis. We demonstrate that remotely sensed temperature estimates may more closely represent true conditions than those that rely on interpolation, especially in regions with sparse in situ data. By leveraging remotely sensed infrared temperature observations, CHIRTS-daily provides estimates of 2-meter air temperature for 1983-2016 with a footprint covering 60°S-70°N. We describe this data set and perform a series of validations using station observations from two prominent climate data sources. The validations indicate high levels of accuracy, with CHIRTS-daily correlations with observations ranging from 0.7 to 0.9, and very good representation of heat wave trends.
ABSTRACT
This corrects the article DOI: 10.1038/sdata.2015.50.
ABSTRACT
The Climate Hazards group Infrared Precipitation with Stations (CHIRPS) dataset builds on previous approaches to 'smart' interpolation techniques and high resolution, long period of record precipitation estimates based on infrared Cold Cloud Duration (CCD) observations. The algorithm i) is built around a 0.05° climatology that incorporates satellite information to represent sparsely gauged locations, ii) incorporates daily, pentadal, and monthly 1981-present 0.05° CCD-based precipitation estimates, iii) blends station data to produce a preliminary information product with a latency of about 2 days and a final product with an average latency of about 3 weeks, and iv) uses a novel blending procedure incorporating the spatial correlation structure of CCD-estimates to assign interpolation weights. We present the CHIRPS algorithm, global and regional validation results, and show how CHIRPS can be used to quantify the hydrologic impacts of decreasing precipitation and rising air temperatures in the Greater Horn of Africa. Using the Variable Infiltration Capacity model, we show that CHIRPS can support effective hydrologic forecasts and trend analyses in southeastern Ethiopia.
ABSTRACT
East Africa is a drought prone, food and water insecure region with a highly variable climate. This complexity makes rainfall estimation challenging, and this challenge is compounded by low rain gauge densities and inhomogeneous monitoring networks. The dearth of observations is particularly problematic over the past decade, since the number of records in globally accessible archives has fallen precipitously. This lack of data coincides with an increasing scientific and humanitarian need to place recent seasonal and multi-annual East African precipitation extremes in a deep historic context. To serve this need, scientists from the UC Santa Barbara Climate Hazards Group and Florida State University have pooled their station archives and expertise to produce a high quality gridded 'Centennial Trends' precipitation dataset. Additional observations have been acquired from the national meteorological agencies and augmented with data provided by other universities. Extensive quality control of the data was carried out and seasonal anomalies interpolated using kriging. This paper documents the CenTrends methodology and data.
