Pete Peterson

The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes

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.

A New Method for Deriving Ocean Surface Specific Humidity and Air Temperature: An Artificial Neural Network Approach

Abstract A new methodology for deriving monthly averages of surface specific humidity (Qa) and air temperature (Ta) is described. Two main aspects characterize the new approach. First, remotely sensed parameters, total precipitable water (W), and sea surface temperature (SST) are used to derive Qa and Ta. Second, artificial neural networks (ANN) are employed to find transfer functions relating the input (W, SST) and output (Qa and Ta) parameters. Input data consist of nearly six years (January 1988–November 1993) of monthly averages of total precipitable water from Special Sensor Microwave/Imager data and sea surface temperature analysis from the National Centers for Environmental Prediction. Surface marine observations of Qa and Ta are used to develop and evaluate the new methodology. The performance of the algorithm is measured with surface marine observations not used in the development phase. Higher seasonally dependent discrepancies between Qa and Ta derived from the new method and in situ data are o...

A land data assimilation system for sub-Saharan Africa food and water security applications

Seasonal agricultural drought monitoring systems, which rely on satellite remote sensing and land surface models (LSMs), are important for disaster risk reduction and famine early warning. These systems require the best available weather inputs, as well as a long-term historical record to contextualize current observations. This article introduces the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS), a custom instance of the NASA Land Information System (LIS) framework. The FLDAS is routinely used to produce multi-model and multi-forcing estimates of hydro-climate states and fluxes over semi-arid, food insecure regions of Africa. These modeled data and derived products, like soil moisture percentiles and water availability, were designed and are currently used to complement FEWS NET’s operational remotely sensed rainfall, evapotranspiration, and vegetation observations. The 30+ years of monthly outputs from the FLDAS simulations are publicly available from the NASA Goddard Earth Science Data and Information Services Center (GES DISC) and recommended for use in hydroclimate studies, early warning applications, and by agro-meteorological scientists in Eastern, Southern, and Western Africa.

Ocean surface air temperature derived from multiple data sets and artificial neural networks

This paper presents a new method to derive monthly averaged surface air temperature, Ta, from multiple data sets. Sea Surface Temperature (SST) from the National Centers for Environmental Prediction (NCEP) and total precipitable water (W) from the SSM/I sensor are used as inputs to Artificial Neural Networks (ANN). Surface air temperature (Ta) measurements from the Surface Marine Data (SMD) are used to develop and evaluate the methodology. When globally evaluated with SMD data, the bias of the new method is small (0.050°C ± 0.26°C), and the accuracy expressed as root-mean square (rms) differences has a small global mean (0.73°C ± 0.37°C). These biases and rms differences are smaller than those obtained using NCEP reanalyses and TIROS Operational Vertical Sounder (TOVS) data products. When evaluated with the TOGA-TAO array measurements over the tropical Pacific, the ANN mean bias and rms differences have similarly small values, 0.37°C and 0.61°C, respectively.

Climatology and Interannual Variability of Boreal Spring Wet Season Precipitation in the Eastern Horn of Africa and Implications for Its Recent Decline

AbstractThe 1981–2014 climatology and variability of the March–May eastern Horn of Africa boreal spring wet season are examined using precipitation, upper- and lower-level winds, low-level specific humidity, and convective available potential energy (CAPE), with the aim of better understanding the establishment of the wet season and the cause of the recent observed decline. At 850 mb, the development of the wet season is characterized by increasing specific humidity and winds that veer from northeasterly in February to southerly in June and advect moisture into the region, in agreement with an earlier study. Equally important, however, is a substantial weakening of the 200-mb climatological easterly winds in March. Likewise, the shutdown of the wet season coincides with the return of strong easterly winds in June. Similar changes are seen in the daily evolution of specific humidity and 200-mb wind when composited relative to the interannual wet season onset and end, with the easterlies decreasing (increas...

Statistical Connection between the Madden–Julian Oscillation and Large Daily Precipitation Events in West Africa

AbstractThis study focuses on the impact of the Madden–Julian oscillation (MJO)—as monitored by a well-known multivariate index—on large daily precipitation events in West Africa for the period 1981–2014. Two seasons are considered: the near-equatorial wet season in March–May (MAM) and the peak of the West African monsoon during July–September (JAS), when the intertropical convergence zone (ITCZ) is at its most northerly position. Although the MJO-related interannual variation of seasonal mean rainfall is large, the focus here is on the impacts of the MJO on daily time scales because variations in the frequency of intense, short-term, flood-causing, rainfall events are more important for West African agriculture than variations in seasonal precipitation, particularly near the Guinean coast, where precipitation is abundant. Using composites based on thresholds of daily precipitation amounts, changes in mean precipitation and frequency of the heaviest daily events associated with the phase of the MJO are in...

Corrigendum: The Centennial Trends Greater Horn of Africa precipitation dataset

This corrects the article DOI: 10.1038/sdata.2015.50.