Geology topics

Martin Landsfeld

Publications and source records attributed to Martin Landsfeld.

6 recordsLinked to original sources

USGS2026

The Climate Hazards Center Infrared Precipitation with Stations, version 3

The Climate Hazards Center Infrared Precipitation with Stations (CHIRPS) data stream combines: (1) a high-resolution climatology, (2) thermal infrared (TIR) geostationary satellite observations, and (3) station observations. In the past, CHIRPS version 2 (CHIRPS2) has proven to be valuable for drought monitoring, hydrologic modeling, scientific studies and agricultural decision making. Version 3 (CHIRPS3) improves each of these components. The new version, CHIRPS3 extends to 60°S/N, adopts an improved variance-preserving TIR-to-precipitation estimation method, uses many more stations and station sources than the original CHIRPS2 product, and implements gauge-undercatch correction. In this paper, we evaluate the performance of satellite-only CHIRP3, CHIRP2, IMERG, PERSIANN- CCS, and GPI using high quality interpolated data in twelve regions with dense station coverage. CHIRP3 represents both the observed mean and variance more accurately than CHIRP2. A usage section in Morocco shows that CHIRPS3 better captures the observed rainfall variability when compared to CHIRPS2. This section also demonstrates how station data should be gauge-undercatch-corrected when validating CHIRPS3.

Scientific Data↗

USGS2021

Enhancing the application of Earth observations for improved environmental decision-making using the Early Warning eXplorer (EWX)

The mitigation of losses due to extreme climate events and long-term climate adaptation requires climate informed decision-making. In the past few decades, several remote sensing and modeled-based Earth observations (EOs) have been developed to provide an unprecedented global overview and routine monitoring of climate and its impacts on vegetation and hydrologic conditions, with the goal of supporting informed decision-making. However, their usage in decision-making is particularly limited in climate-risk vulnerable and in situ data-scarce regions such as sub-Saharan Africa, due to lack of access to EOs. Here, we describe the Early Warning eXplorer (EWX), which was developed to address this crucial limitation and facilitate the application of EOs in decision-making, particularly in the food and water-insecure regions of the world. First, the EWX's core framework, which includes (i) the Viewer, (ii) GeoEngine, and (iii) Support Applications, is described. Then, a comprehensive overview of the Viewer, which is a web-based interface used to access EOs, is provided. This includes a description of (i) the maps and associated features to access gridded EO data and anomalies for different temporal averaging periods, (ii) time series graphs and associated features to access EOs aggregated over polygons such as administrative boundaries, and (iii) commonly used EOs served by the EWX that provide assessments of climate and vegetation conditions. Next, examples are provided to demonstrate how EWX can be used to monitor development, progression, spatial extent, and severity of climate-driven extreme events to support timely decisions related to mitigation of food insecurity and flooding impacts. Finally, the value of a regional implementation of EWX at the Regional Centre for Mapping of Resources for Development (RCMRD) in Nairobi, Kenya, is highlighted. Regional implementation of the EWX facilitates access to regionally focused EOs and their availability at polygon boundaries most relevant to the local decision-makers. Similar instances of EWX implemented in other regions, especially those susceptible to food and water security, will likely further enhance the application of EOs for informed decision-making.

Frontiers in Climate↗

USGS2020

Development and validation of the CHIRTS-daily quasi-global high-resolution daily temperature data set

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.

Scientific Data↗

USGS2020

Algorithm and data improvements for version 2.1 of the Climate Hazards center’s InfraRed Precipitation with Stations Data Set

To support global drought early warning, the Climate Hazards Center (CHC) at the University of California, Santa Barbara developed the Climate Hazards center InfraRed Precipitation with Stations (CHIRPS) dataset, in collaboration with the US Geological Survey and NASA SERVIR. Specifically designed to support early warning applications, CHIRPS has high a spatial resolution (0.05°), a long period of record (1981 to the near present), and relatively low latencies. Here we will describe a brief formal analysis of distributional bias in CHIRPS2.0. This analysis reveals, as expected, that CHIRPS2.0 means are very similar to observed station data. However, a closer look suggests that low precipitation values are underestimated and high values are over-estimated in the CHIRPS2.0. We describe a potential correction for this below.

Book chapter↗

USGS2019

A high-resolution 1983-2016 Tmax climate data record based on InfraRed Temperatures and Stations by the Climate Hazard Center

Understanding the dynamics and physics of climate extremes will be a critical challenge for 21st century climate science. Increasing temperatures and saturation vapor pressures may exacerbate heat waves, droughts and precipitation extremes. Yet our ability to monitor temperature variations is limited and declining. Between 1983 and 2016 the number of observations in the CRU Tmax product declined precipitously (5,900→1,000); 1,000 poorly distributed measurements are not enough to resolve regional Tmax variations. Here we show that long (1983-near present) high resolution (0.05°), cloud-screened archives of geostationary satellite Thermal Infrared Red (TIR) observations, when combined with a dense set of ~15,000 station observations, can explain 23, 40, 30, 11% more variance than the CRU over the globe, South America, Africa, India and areas north of 50°N, with even greater levels of improvement over the 2011-2016 period (28, 45, 39, 52, 28%). Described here for the first time, the TIR Tmax algorithm uses sub-daily TIR distributions to screen out cloud contaminated observations, providing accurate (correlation≈0.8) gridded emission Tmax estimates. Blending these gridded fields with ~15,000 station observations provides a seamless, high-resolution source of accurate Tmax estimates that performs well in areas lacking dense in situ observations and even better where in situ observations are available. Cross-validation results indicates that the satellite-only, station-only and combined products all perform accurately (R≈0.8-0.9, mean absolute errors ≈0.8-1.0). Hence, the Climate Hazards center InfraRed Temperature with Stations (CHIRTSmax) data set should provide a valuable resource for climate change studies, climate extreme analyses, and early warning applications.

Journal of Climate↗

USGS2015

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.

Scientific Data↗