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A short-term meteorological database has been developed for the Cold Land Processes Experiment (CLPX). This database includes meteorological observations from stations designed and deployed exclusively for CLPX as well as observations available from other sources located in the small regional study area (SRSA) in north-central Colorado. The measured weather parameters include air temperature, relative humidity, wind speed and direction, barometric pressure, short- and longwave radiation, leaf wetness, snow depth, snow water content, snow and surface temperatures, volumetric soil moisture content, soil temperature, precipitation, water vapor flux, carbon dioxide flux, and soil heat flux. The CLPX weather stations include 10 main meteorological towers, 1 tower within each of the nine intensive study areas (ISA) and one near the local scale observation site (LSOS); and 36 simplified towers, with one tower at each of the four corners of each of the nine ISAs, which measured a reduced set of parameters. An eddy covariance system within the North Park mesocell study area (MSA) collected a variety of additional parameters beyond the 10 standard CLPX tower components. Additional meteorological observations come from a variety of existing networks maintained by the U.S. Forest Service, U.S. Geological Survey, Natural Resource Conservation Service, and the Institute of Arctic and Alpine Research. Temporal coverage varies from station to station, but it is most concentrated during the 2002/03 winter season. These data are useful in local meteorological energy balance research and for model development and testing. These data can be accessed through the National Snow and Ice Data Center Web site. [ABSTRACT FROM AUTHOR]

The article discusses the Land Information System (LIS), a multiscale infrastructure for hydrologic modeling and data assimilation which was developed by the United States' National Aeronautics & Space Administration. LIS integrates satellite and ground-based observations and land-surface modeling methods to support applications that can be used in water-resource and agricultural management, statistical weather prediction, and assessment of air quality and military mobility. The discussion focuses on the features of a land data assimilation system such as its software architecture and high-computing technology, its modularity, reusability, extensibility, and inversion control, and its Earth system modeling standards.

The Coordinated Enhanced Observing Period (CEOP) is an international project that was first proposed by the Global Energy and Water Cycle Experiment (GEWEX) in 1997 and was formally launched in 2001. Since that time it has been adopted by the World Climate Research Programme (WCRP), which views it as an essential part of its strategy for developing global datasets to evaluate global climate models, and by the Integrated Global Observing Strategy Partnership (IGOS-P), which views it as the first element of its global water cycle theme. The United States has been an active partner in all phases of CEOP. In particular, the United States has taken the lead in contributing data from a number of reference sites, providing data processing, and archiving capabilities and related research activities through the GEWEX Americas Prediction Project (GAPP). Other U.S. programs and agencies are providing components including model and data assimilation output, satellite data, and other services. The U.S. science community has also been using the CEOP database in model evaluation and phenomenological studies. This article summarizes the U.S. contributions during the first phase of CEOP and outlines opportunities for readers to become involved in the data analysis phase of the project. [ABSTRACT FROM AUTHOR]

Precipitation is arguably the most important meteorological forcing variable in land surface modeling. Many types of precipitation datasets exist (with various pros and cons) and include those from atmospheric data assimilation systems, satellites, rain gauges, ground radar, and merged products. These datasets are being evaluated in order to choose the most suitable precipitation forcing for real-time and retrospective simulations of the Global Land Data Assimilation System (GLDAS). This paper first presents results of a comparison for the period from March 2002 to February 2003. Later, GLDAS simulations 14 months in duration are analyzed to diagnose impacts on GLDAS land surface states when using the Mosaic land surface model (LSM). A comparison of seasonal total precipitation for the continental United States (CONUS) illustrates that the Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP) has the closest agreement with a CPC rain gauge dataset for all seasons except winter. The European Centre for Medium-Range Weather Forecasts (ECMWF) model performs the best of the modeling systems. The satellite-only products [the Tropical Rainfall Measuring Mission (TRMM) Real-time Multi-satellite Precipitation Analysis and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN)] suffer from a few deficiencies—most notably an overestimation of summertime precipitation in the central United States (200–400 mm). CMAP is the most closely correlated with daily rain gauge data for the spring, fall, and winter seasons, while the satellite-only estimates perform best in summer. GLDAS land surface states are sensitive to different precipitation forcing where percent differences in volumetric soil water content (SWC) between simulations ranged from -75% to +100%. The percent differences in SWC are generally 25%–75% less than the percent precipitation differences, indicating that GLDAS and specifically the Mosaic LSM act to generally “damp” precipitation differences. Areas where the percent changes are equivalent to the percent precipitation changes, however, are evident. Soil temperature spread between GLDAS runs was considerable and ranged up to ±3.0 K with the largest impact in the western United States. [ABSTRACT FROM AUTHOR]

Based on 1-km land surface model geophysical predictions within the United States Southern Great Plains (Red-Arkansas River basin), an observing system simulation experiment (OSSE) is carried out to assess the impact of land surface heterogeneity, instrument error, and parameter uncertainty on soil moisture products derived from the National Aeronautics and Space Administration Hydrosphere State (Hydros) mission. Simulated retrieved soil moisture products are created using three distinct retrieval algorithms based on the characteristics of passive microwave measurements expected from Hydros. The accuracy of retrieval products is evaluated through comparisons with benchmark soil moisture fields obtained from direct aggregation of the original simulated soil moisture fields. The analysis provides a quantitative description of how land surface heterogeneity, instrument error, and inversion parameter uncertainty impacts propagate through the measurement and retrieval process to degrade the accuracy of Hydros soil moisture products. Results demonstrate that the discrete set of error sources captured by the OSSE induce root mean squared errors of between 2.0% and 4.5 % volumetric in soil moisture retrievals within the basin. Algorithm robustness is also evaluated for the case of artificially enhanced vegetation water content (W) values within the basin. For large W(> 3 kg . m-2), a distinct positive bias, attributable to the impact of sub-footprint-scale landcover heterogeneity, is identified in soil moisture retrievals. Prospects for the removal of this bias via a correction strategy for inland water and/or the implementation of an alternative aggregation strategy for surface vegetation and roughness parameters are discussed. [ABSTRACT FROM AUTHOR]

The coronavirus disease 2019 (COVID-19) has been first reported in December 2019 and rapidly spread worldwide. As other severe acute respiratory syndromes, it is a widely discussed topic whether seasonality affects the COVID-19 infection spreading. This study presents two different approaches to analyse the impact of social activity factors and weather variables on daily COVID-19 cases at county level over the Continental U.S. (CONUS). The first one is a traditional statistical method, i.e., Pearson correlation coefficient, whereas the second one is a machine learning algorithm, i.e., random forest regression model. The Pearson correlation is analysed to roughly test the relationship between COVID-19 cases and the weather variables or the social activity factor (i.e. social distance index). The random forest regression model investigates the feasibility of estimating the number of county-level daily confirmed COVID-19 cases by using different combinations of eight factors (county population, county population density, county social distance index, air temperature, specific humidity, shortwave radiation, precipitation, and wind speed). Results show that the number of daily confirmed COVID-19 cases is weakly correlated with the social distance index, air temperature and specific humidity through the Pearson correlation method. The random forest model shows that the estimation of COVID-19 cases is more accurate with adding weather variables as input data. Specifically, the most important factors for estimating daily COVID-19 cases are the population and population density, followed by the social distance index and the five weather variables, with temperature and specific humidity being more critical than shortwave radiation, wind speed, and precipitation. The validation process shows that the general values of correlation coefficients between the daily COVID-19 cases estimated by the random forest model and the observed ones are around 0.85. • This study investigates the impact of social activity factors and weather variables on COVID-19 transmission in the U.S. • The random forest model shows the estimation of COVID-19 cases is more accurate with adding weather variables as input data. • The random forest model estimated number of COVID-19 cases are highly correlated with the observation data. [ABSTRACT FROM AUTHOR]

Captive breeding programs are a valuable conservation strategy, particularly when integrated with research goals. Panamanian Harlequin frogs (genus Atelopus) serve as a case study for integrating captive breeding and research goals because they have experienced drastic chytridiomycosis-related declines and have large captive populations. Captive breeding efforts in Panama and the United States established secure ex-situ populations of Atelopus certus , A. glyphus , A. limosus , A. varius , and A. zeteki. Atelopus chiriquiensis is presumed to be extinct with no captive populations. The status of one undescribed species, Atelopus aff. limosus , has not been evaluated and no secure captive population has yet been established. Captive breeding efforts that produce a surplus of Atelopus are an important resource for research into collections management, disease mitigation, and adaptive management approaches for Atelopus reintroduction efforts. We reevaluated all Panamanian Atelopus species through the IUCN Redlist and compiled occurrence records for Panamanian Atelopus species to create a historical distribution map. We model Atelopus habitat suitability using Maxent and found annual mean air temperature to be the best predictor of Atelopus occurrence. The model will improve our knowledge of their likely spatial distribution and guide future conservation and reintroduction efforts. The recent proliferation of molecular tools, climate models, bio-banking, and reproductive technologies position us to address multiple applied and basic evolutionary questions such as: What factors cause differential disease outcomes? Do persisting populations have heritable traits associated with improved survivorship? Are there climatic refugia from disease? Ultimately, the answers to these questions will help us develop applied solutions and facilitate the reestablishment of self-sustaining wild populations. • Atelopus species have experienced severe chytridiomycosis-related declines. • This case study documents the wild and captive status of Atelopus in Panama. • Habitat models improve historical distribution maps and guide future efforts. • Captive breeding efforts prevent extinctions and are a valuable research resource. [ABSTRACT FROM AUTHOR]