Multiscale assimilation of Advanced Microwave Scanning Radiometer-EOS snow water equivalent and Moderate Resolution Imaging Spectroradiometer snow cover fraction observations in northern Colorado

Eight years (2002–2010) of Advanced Microwave Scanning Radiometer–EOS (AMSR-E) snow water equivalent (SWE) retrievals and Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover fraction (SCF) observations are assimilated separately or jointly into the Noah land surface model over a domain in Northern Colorado. A multiscale ensemble Kalman filter (EnKF) is used, supplemented with a rule-based update. The satellite data are either left unscaled or are scaled for anomaly assimilation. The results are validated against in situ observations at 14 high-elevation Snowpack Telemetry (SNOTEL) sites with typically deep snow and at 4 lower-elevation Cooperative Observer Program (COOP) sites. Assimilation of coarse-scale AMSR-E SWE and fine-scale MODIS SCF observations both result in realistic spatial SWE patterns. At COOP sites with shallow snowpacks, AMSR-E SWE and MODIS SCF data assimilation are beneficial separately, and joint SWE and SCF assimilation yields significantly improved root-mean-square error and correlation values for scaled and unscaled data assimilation. In areas of deep snow where the SNOTEL sites are located, however, AMSR-E retrievals are typically biased low and assimilation without prior scaling leads to degraded SWE estimates. Anomaly SWE assimilation could not improve the interannual SWE variations in the assimilation results because the AMSR-E retrievals lack realistic interannual variability in deep snowpacks. SCF assimilation has only a marginal impact at the SNOTEL locations because these sites experience extended periods of near-complete snow cover. Across all sites, SCF assimilation improves the timing of the onset of the snow season but without a net improvement of SWE amounts

Inferring Land Surface Model Parameters for the Assimilation of Satellite-Based L-Band Brightness Temperature Observations into a Soil Moisture Analysis System

The Soil Moisture and Ocean Salinity (SMOS) satellite mission provides global measurements of L-band brightness temperatures at horizontal and vertical polarization and a variety of incidence angles that are sensitive to moisture and temperature conditions in the top few centimeters of the soil. These L-band observations can therefore be assimilated into a land surface model to obtain surface and root zone soil moisture estimates. As part of the observation operator, such an assimilation system requires a radiative transfer model (RTM) that converts geophysical fields (including soil moisture and soil temperature) into modeled L-band brightness temperatures. At the global scale, the RTM parameters and the climatological soil moisture conditions are still poorly known. Using look-up tables from the literature to estimate the RTM parameters usually results in modeled L-band brightness temperatures that are strongly biased against the SMOS observations, with biases varying regionally and seasonally. Such biases must be addressed within the land data assimilation system. In this presentation, the estimation of the RTM parameters is discussed for the NASA GEOS-5 land data assimilation system, which is based on the ensemble Kalman filter (EnKF) and the Catchment land surface model. In the GEOS-5 land data assimilation system, soil moisture and brightness temperature biases are addressed in three stages. First, the global soil properties and soil hydraulic parameters that are used in the Catchment model were revised to minimize the bias in the modeled soil moisture, as verified against available in situ soil moisture measurements. Second, key parameters of the "tau-omega" RTM were calibrated prior to data assimilation using an objective function that minimizes the climatological differences between the modeled L-band brightness temperatures and the corresponding SMOS observations. Calibrated parameters include soil roughness parameters, vegetation structure parameters, and the single scattering albedo. After this climatological calibration, the modeling system can provide L-band brightness temperatures with a global mean absolute bias of less than 10K against SMOS observations, across multiple incidence angles and for horizontal and vertical polarization. Third, seasonal and regional variations in the residual biases are addressed by estimating the vegetation optical depth through state augmentation during the assimilation of the L-band brightness temperatures. This strategy, tested here with SMOS data, is part of the baseline approach for the Level 4 Surface and Root Zone Soil Moisture data product from the planned Soil Moisture Active Passive (SMAP) satellite mission

Assessment and enhancement of MERRA land surface hydrology estimates

The Modern-Era Retrospective Analysis for Research and Applications (MERRA) is a state-of-the-art reanalysis that provides, in addition to atmospheric fields, global estimates of soil moisture, latent heat flux, snow, and runoff for 1979 present. This study introduces a supplemental and improved set of land surface hydrological fields ("MERRA-Land") generated by rerunning a revised version of the land component of the MERRA system. Specifically, the MERRA-Land estimates benefit from corrections to the precipitation forcing with the Global Precipitation Climatology Project pentad product (version 2.1) and from revised parameter values in the rainfall interception model, changes that effectively correct for known limitations in the MERRA surface meteorological forcings. The skill (defined as the correlation coefficient of the anomaly time series) in land surface hydrological fields from MERRA and MERRA-Land is assessed here against observations and compared to the skill of the state-of-the-art ECMWF Re-Analysis-Interim (ERA-I). MERRA-Land and ERA-I root zone soil moisture skills (against in situ observations at 85 U.S. stations) are comparable and significantly greater than that of MERRA. Throughout the Northern Hemisphere, MERRA and MERRA-Land agree reasonably well with in situ snow depth measurements (from 583 stations) and with snow water equivalent from an independent analysis. Runoff skill (against naturalized stream flow observations from 18 U.S. basins) of MERRA and MERRA-Land is typically higher than that of ERA-I. With a few exceptions, the MERRA-Land data appear more accurate than the original MERRA estimates and are thus recommended for those interested in using MERRA output for land surface hydrological studies

SMAP Data Assimilation at the GMAO

The NASA Soil Moisture Active Passive (SMAP) mission has been providing L-band (1.4 GHz) passive microwave brightness temperature (Tb) observations since April 2015. These observations are sensitive to surface(0-5 cm) soil moisture. Several of the key applications targeted by SMAP, however, require knowledge of deeper-layer, root zone (0-100 cm) soil moisture, which is not directly measured by SMAP. The NASA Global Modeling and Assimilation Office (GMAO) contributes to SMAP by providing Level 4 data, including the Level 4 Surface and Root Zone Soil Moisture(L4_SM) product, which is based on the assimilation of SMAP Tb observations in the ensemble-based NASA GEOS-5 land surface data assimilation system. The L4_SM product offers global data every three hours at 9 km resolution, thereby interpolating and extrapolating the coarser- scale (40 km) SMAP observations in time and in space (both horizontally and vertically). Since October 31, 2015, beta-version L4_SM data have been available to the public from the National Snow and Ice Data Center for the period March 31, 2015, to near present, with a mean latency of approx. 2.5 days

A Dynamic Approach to Addressing Observation-Minus-Forecast Mean Differences in a Land Surface Skin Temperature Data Assimilation System

In land data assimilation, bias in the observation-minus-forecast (O-F) residuals is typically removed from the observations prior to assimilation by rescaling the observations to have the same long-term mean (and higher-order moments) as the corresponding model forecasts. Such observation rescaling approaches require a long record of observed and forecast estimates, and an assumption that the O-F mean differences are stationary. A two-stage observation bias and state estimation filter is presented, as an alternative to observation rescaling that does not require a long data record or assume stationary O-F mean differences. The two-stage filter removes dynamic (nonstationary) estimates of the seasonal scale O-F mean difference from the assimilated observations, allowing the assimilation to correct the model for synoptic-scale errors without adverse effects from observation biases. The two-stage filter is demonstrated by assimilating geostationary skin temperature (Tsk) observations into the Catchment land surface model. Global maps of the O-F mean differences are presented, and the two-stage filter is evaluated for one year over the Americas. The two-stage filter effectively removed the Tsk O-F mean differences, for example the GOES-West O-F mean difference at 21:00 UTC was reduced from 5.1 K for a bias-blind assimilation to 0.3 K. Compared to independent in situ and remotely sensed Tsk observations, the two-stage assimilation reduced the unbiased Root Mean Square Difference (ubRMSD) of the modeled Tsk by 10 of the open-loop values

A new species of arboreal toad (Anura: Bufonidae: Chaunus) from Madidi National Park, Bolivia

Accepted by M. Vences: 23 Jun 2006; published: 3 Aug. 2006A new arboreal species of the Chaunus veraguensis group is described for the humid montane forest of Madidi National Park, in northern Bolivia. The new species differs from other species in the group by the combination small size, long and slender extremities, webbed hands, conspicuous tympanic membrane, well developed parotoid glands, absence of large glands on dorsum and extremities, nuptial excrescences of males composed of pungent spines on dorsal surface of thumb, greenish-brown coloration on dorsum with red warts in life, and green iris. It is only known from two nearby localities in the Serranía Eslabón, Department La Paz. An operational key for species in the C. veraguensis group is provided.This work was partially funded by projects REN/GLO 2001-1046 and CGL2005-03156 of the Spanish Ministry of Education and Science (I. De la Riva, Principal Investigator) and by a project of the AECI (Spanish Agency of International Cooperation) (I. De la Riva, Principal Investigator) to inventory Madidi’s herpetofauna.Peer reviewe

Soil Moisture Data Assimilation

Accurate knowledge of soil moisture at the continental scale is important for improving predictions of weather, agricultural productivity and natural hazards, but observations of soil moisture at such scales are limited to indirect measurements, either obtained through satellite remote sensing or from meteorological networks. Land surface models simulate soil moisture processes, using observation-based meteorological forcing data, and auxiliary information about soil, terrain and vegetation characteristics. Enhanced estimates of soil moisture and other land surface variables, along with their uncertainty, can be obtained by assimilating observations of soil moisture into land surface models. These assimilation results are of direct relevance for the initialization of hydro-meteorological ensemble forecasting systems. The success of the assimilation depends on the choice of the assimilation technique, the nature of the model and the assimilated observations, and, most importantly, the characterization of model and observation error. Systematic differences between satellite-based microwave observations or satellite-retrieved soil moisture and their simulated counterparts require special attention. Other challenges include inferring root-zone soil moisture information from observations that pertain to a shallow surface soil layer, propagating information to unobserved areas and downscaling of coarse information to finer-scale soil moisture estimates. This chapter summarizes state-of-the-art solutions to these issues with conceptual data assimilation examples, using techniques ranging from simplified optimal interpolation to spatial ensemble Kalman filtering. In addition, operational soil moisture assimilation systems are discussed that support numerical weather prediction at ECMWF and provide value-added soil moisture products for the NASA Soil Moisture Active Passive mission

Nuevas citas de Eleutherodactylus Duméril y Bibron, 1841 (Anura, Leptodactylidae) para Bolivia

Five species of the genus Eleutherodactylus (Anura, Leptodactylidae) are reported from Bolivia for the first time: E. altamazonicus from Departments Pando and La Paz; E. carvalhoi from Cochabamba; and E. ockendeni, E. skydmainos and E. zimmermanae from Pando. New departmental records are provided for other species: E. cruralis for Department Pando, E. danae for Beni and La Paz, E. platydactylus for Beni, E. toftae for Pando and La Paz and E. ventrimarmoratus for La Paz. Currently, 25 species of Eleutherodactylus are known in Bolivia. The taxonomic status of some species remains uncertain, new species are being described and others are still to be discovered. Therefore, the actual number of Bolivian Eleutherodactylus species is still unknown.En este trabajo se publican los primeros registros de cinco especies del género Eleutherodactylus (Anura, Leptodactylidae) para Bolivia: E. altamazonicus en los Departamentos de Pando y La Paz; E. carvalhoi en Cochabamba; y E. ockendeni, E. skydmainos y E. zimmermanae en Pando. Además, se aportan las primeras citas de E. cruralis para el Departamento de Pando, de E. danae para Beni y La Paz, de E. platydactylus para Beni, de E. toftae para Pando y La Paz y de E. ventrimarmoratus para La Paz. La diversidad conocida de Eleutherodactylus en Bolivia es de 25 especies, aunque los problemas taxonómicos sin resolver, las especies en descripción y el alto número de especies aún no citadas que se considera probable encontrar, hacen que aún desconozcamos la verdadera diversidad de este género en Bolivia

Joint Assimilation of SMOS Brightness Temperature and GRACE Terrestrial Water Storage Observations for Improved Soil Moisture Estimation

Observations from recent soil moisture missions (e.g. SMOS) have been used in innovative data assimilation studies to provide global high spatial (i.e. 40 km) and temporal resolution (i.e. 3-days) soil moisture profile estimates from microwave brightness temperature observations. In contrast with microwave-based satellite missions that are only sensitive to near-surface soil moisture (0 - 5 cm), the Gravity Recovery and Climate Experiment (GRACE) mission provides accurate measurements of the entire vertically integrated terrestrial water storage column but, it is characterized by low spatial (i.e. 150,000 km2) and temporal (i.e. monthly) resolutions. Data assimilation studies have shown that GRACE-TWS primarily affects (in absolute terms) deeper moisture storages (i.e., groundwater). This work hypothesizes that unprecedented soil water profile accuracy can be obtained through the joint assimilation of GRACE terrestrial water storage and SMOS brightness temperature observations. A particular challenge of the joint assimilation is the use of the two different types of measurements that are relevant for hydrologic processes representing different temporal and spatial scales. The performance of the joint assimilation strongly depends on the chosen assimilation methods, measurement and model error spatial structures. The optimization of the assimilation technique constitutes a fundamental step toward a multi-variate multi-resolution integrative assimilation system aiming to improve our understanding of the global terrestrial water cycle

Predicting \u3cem\u3eLeptodactylus\u3c/em\u3e (Amphibia, Anura, Leptodactylidae) Distributions: Broad-Ranging Versus Patchily Distributed Species Using a Presence-Only Environmental Niche Modeling Technique

Locality data available for many, if not most, species of Neotropical frogs are based on written descriptions of the collecting sites, not on GPS device determined coordinate data. The pre-GPS device data are imprecise relative to GPS data. Niche modeling is a powerful technique for predicting geographic distributions that provides the best results when the locality data are precise. The purpose of this study is to determine whether imprecise historical locality data are sufficient such that niche modeling techniques can yield realistic new insights to species-level distributions. Two sets of frogs of the genus Leptodactylus that have known different kinds of distributions are evaluated: two species with broad, presumably continuous distributions, and four species known to occur in patchy, disjunct habitats in South America. BIOCLIM, a presence-only environmental niche modeling algorithm, was used to define suitable occupancy areas based on multiple sets of environmental parameters that include: monthly mean, max, and min temperatures, and monthly precipitation. A Nature Conservancy - Natureserve ecoregion layer and a high resolution elevation layer were also included in the analyses. Our analyses yield new realistic insights and questions regarding distributions of the Leptodactylus species we evaluated. We recommend incorporation of the Nature Conservancy- Natureserve layer to evaluate Neotropical distributions, as the layer gave much more robust results than use of only the climatic variable analyses