Abstract

Abstract. Accurate representations of mean climate conditions, especially in areas of complex terrain, are an important part of environmental monitoring systems. As high-resolution satellite monitoring information accumulates with the passage of time, it can be increasingly useful in efforts to better characterize the earth's mean climatology. Current state-of-the-science products rely on complex and sometimes unreliable relationships between elevation and station-based precipitation records, which can result in poor performance in food and water insecure regions with sparse observation networks. These vulnerable areas (like Ethiopia, Afghanistan, or Haiti) are often the critical regions for humanitarian drought monitoring. Here, we show that long period of record geo-synchronous and polar-orbiting satellite observations provide a unique new resource for producing high-resolution (0.05°) global precipitation climatologies that perform reasonably well in data-sparse regions. Traditionally, global climatologies have been produced by combining station observations and physiographic predictors like latitude, longitude, elevation, and slope. While such approaches can work well, especially in areas with reasonably dense observation networks, the fundamental relationship between physiographic variables and the target climate variables can often be indirect and spatially complex. Infrared and microwave satellite observations, on the other hand, directly monitor the earth's energy emissions. These emissions often correspond physically with the location and intensity of precipitation. We show that these relationships provide a good basis for building global climatologies. We also introduce a new geospatial modeling approach based on moving window regressions and inverse distance weighting interpolation. This approach combines satellite fields, gridded physiographic indicators, and in situ climate normals. The resulting global 0.05° monthly precipitation climatology, the Climate Hazards Group's Precipitation Climatology version 1 (CHPclim v.1.0, doi:10.15780/G2159X), is shown to compare favorably with similar global climatology products, especially in areas with complex terrain and low station densities.

Highlights

  • Systematic spatial variations in climate have been studied since at least the first century AD, when Ptolemy’s Geographia identified the earth’s polar, temperate, and equatorial temperature zones

  • A modified inverse-distance weighting (IDW) procedure, similar to Eq (3), is used, but instead of relaxing to zero, the interpolation is forced to a ratio of 1 as the distance to the minimum neighbor reaches dIDW

  • Regions beyond 60◦ N and 60◦ S could not be modeled with the Tropical Rainfall Measuring Mission (TRMM) or Center morphing method (CMORPH) means

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Summary

Introduction

Systematic spatial variations in climate have been studied since at least the first century AD, when Ptolemy’s Geographia identified the earth’s polar, temperate, and equatorial temperature zones Analysis of these climatological surfaces continues to be an important aspect of environmental monitoring and modeling. This new approach combines satellite fields, gridded physiographic indicators, and in situ climate normals using local moving window regressions and inverse distance weighting interpolation. The climatologies are compared with each other, and with a gridded validation data set in Ethiopia

Precipitation normals
Topographic and physiographic surfaces
Localized correlation estimates
Localized moving window regressions
Model fitting
Interpolation of model residuals
Rescaling by GHCN ratios
Cross-validation
Independent validation studies
Model fitting results
Validation studies
Product comparisons
An Ethiopian validation study
Discussion
Full Text
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