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This study characterizes the climate structure in the Eastern United States for suitability of winegrape growth. For this study, the Eastern US is defined as the 44 contiguous Eastern most states. This excludes the premium wine growing states of California, Washington, Oregon and Idaho. For this characterization, a comparative study is performed on the four commonly used climate-viticulture indices (i.e., Average Growing Season Temperature, Growing Degree Days, Heliothermal Index and Biologically Effective Degree Days), and a new climate-viticulture index, the Modified-GSTavg (Mod-GSTavg). This is accomplished using the 1971 – 2000 PRISM 800-meter resolution dataset of climate temperature normal for the study area of 44 states and 62 American Viticultural Areas across the Eastern United States. The results revealed that all the climate indices have similar spatial patterns throughout the US with varying magnitudes and degrees of suitability.

This study investigated the spatial dynamics of water quality across the Shenandoah River basin using spectroscopy and chemometrics to estimate chlorophyll (Chl), colored dissolved organic matter (CDOM) and turbidity using three band combinations and nutrients (total nitrogen and total phosphorous) in the Shenandoah River. The mean Chl a concentration for 555 nm, 560 nm and 640 nm were; 0.31 µg/l, 0.33 µg/l, and 0.51 µg/l respectively. Chlorophyll a showed strong correlations at band 640 (r = 0.92). The bands centered at 670/490 were the best in predicting CDOM and turbidity in the Shenandoah River Basin with an r2 = 0.56. Chemometrics analysis show that total phosphorous, nitrogen and turbidity can be predicted between 450 to 555nm and 670 to 710 nm, the range of wavelengths which indicated better predictability for spectroscopic analysis. The resultant concentration is used to develop predictive models to determine sensitive spectral variables for nitrogen, phosphorous, Chl-a, and CDOM.

This second paper of the two‐part series focuses on demonstrating the impact of assimilating satellite‐based snow cover and freeze/thaw observations into the hyper‐resolution, offline terrestrial modeling system used for the High Mountain Asia (HMA) region from 2003 to 2016. To this end, this study systematically evaluates a total of six sets of 0.01° (∼1 km) model simulations forced by different precipitation forcings, with and without the dual assimilation scheme enabled, at point‐scale, basin‐scale, and domain‐scale. The key variables of interest include surface net shortwave radiation, surface net longwave radiation, skin temperature, near‐surface soil temperature, snow depth, snow water equivalent (SWE), and total runoff. First, the point‐scale assessment is mainly conducted via evaluating against ground‐based measurements. In general, the assimilation enabled estimates are better than no‐assimilation counterparts. Second, the basin‐scale runoff assessment demonstrates that across three snow‐dominated basins, the assimilation enabled experiment yields systematic improvements in all goodness‐of‐fit statistics through mitigating the negative effects brought by the fixed long‐term precipitation correction factors. For example, when forced by the bias‐corrected precipitation, the assimilation‐enabled experiment improves the bias by 69%, the root‐mean‐squared error by 30%, and the unbiased root‐mean‐squared error by 18% (relative to the no‐assimilation counterpart). Finally, the domain‐scale assessment is conducted via evaluating against satellite‐based SWE and skin temperature products. Both sets of domain‐scale analysis further corroborate the findings in the point‐scale evaluations. Overall, this study suggests the benefits of the proposed multi‐variate assimilation system in improving the cryospheric‐hydrological process within a land surface model for use in HMA. The skills of the High Mountain Asia‐Land Data Assimilation System (version 1) with and without multi‐variate assimilation are presentedThe assimilation enabled experiments generally perform better than the no‐assimilation counterpartsThe assimilation enabled experiments mitigate the negative effects arising from the fixed long‐term precipitation correction factors The skills of the High Mountain Asia‐Land Data Assimilation System (version 1) with and without multi‐variate assimilation are presented The assimilation enabled experiments generally perform better than the no‐assimilation counterparts The assimilation enabled experiments mitigate the negative effects arising from the fixed long‐term precipitation correction factors

Two data assimilation (DA) methods, a simple rule‐based direct insertion (DI) approach and a one‐dimensional ensemble Kalman filter (EnKF) method, are evaluated by assimilating snow cover fraction observations into the Community Land surface Model. The ensemble perturbation needed for the EnKF resulted in negative snowpack biases. Therefore, a correction is made to the ensemble bias using an approach that constrains the ensemble forecasts with a single unperturbed deterministic LSM run. This is shown to improve the final snow state analyses. The EnKF method produces slightly better results in higher elevation locations, whereas results indicate that the DI method has a performance advantage in lower elevation regions. In addition, the two DA methods are evaluated in terms of their overall impacts on the other land surface state variables (e.g., soil moisture) and fluxes (e.g., latent heat flux). The EnKF method is shown to have less impact overall than the DI method and causes less distortion of the hydrological budget. However, the land surface model adjusts more slowly to the smaller EnKF increments, which leads to smaller but slightly more persistent moisture budget errors than found with the DI updates. The DI method can remove almost instantly much of the modeled snowpack, but this also allows the model system to quickly revert to hydrological balance for nonsnowpack conditions. Snow cover assimilated via direct insertion(DI) and ensemble Kalman filter(EnKF)EnKF method performed better in higher elevations and DI in lower elevationsVersus DI, EnKF leads to smaller model impacts but more hydrologic budget errors

An instrument for making rapid measurements of the soil moisture content in the root zone is an essential tool for many applications, including understanding of soil water dynamics, evaluation of agriculture water stress, and validation of soil moisture modeling. Studies have shown that electrical resistance measurements may be used to infer soil moisture content under special circumstances. In this paper, electrical resistivity (resistance multiplied by a geometric factor) measurements of the soil by the Geometrics Inc. OhmMapper instrument are compared with point measurements of soil moisture to a depth of 70 cm. It was found that the OhmMapper resistivity measurements could be used to infer soil moisture content with a coefficient of determination as high as 0.34 when a simple power law relationship was used. A more sophisticated analysis of the resistivity measurements could potentially lead to a greater coefficient of determination.

This first paper of the two‐part series focuses on demonstrating the accuracy of a hyper‐resolution, offline terrestrial modeling system used for the High Mountain Asia (HMA) region. To this end, this study systematically evaluates four sets of model simulations at point scale, basin scale, and domain scale obtained from different spatial resolutions including 0.01° (∼1‐km) and 0.25° (∼25‐km). The assessment is conducted via comparisons against ground‐based observations and satellite‐derived reference products. The key variables of interest include surface net shortwave radiation, surface net longwave radiation, skin temperature, near‐surface soil temperature, snow depth, snow water equivalent, and total runoff. In the evaluation against ground‐based measurements, the superiority of the 0.01° estimates are mostly demonstrated across relatively complex terrain. Specifically, hyper‐resolution modeling improves the skill in meteorological forcing estimates (except precipitation) by 9% relative to coarse‐resolution estimates. The model forced by downscaled forcings in its entirety yields the highest skill in model output states as well as precipitation, which improves the skill obtained by coarse‐resolution estimates by 7%. These findings, on one hand, corroborate the importance of employing the hyper‐resolution versus coarse‐resolution modeling in areas characterized by complex terrain. On the other hand, by evaluating four sets of model simulations forced with different precipitation products, this study emphasizes the importance of accurate hyper‐resolution precipitation products to drive model simulations. The skill of a hyper‐resolution, off‐line terrestrial modeling system used for the High Mountain Asia region is presentedThe study emphasizes the importance of using hyper‐resolution versus coarse‐resolution modeling in areas characterized by complex terrainThe study emphasizes the importance of an accurate hyper‐resolution precipitation product used to drive model simulations The skill of a hyper‐resolution, off‐line terrestrial modeling system used for the High Mountain Asia region is presented The study emphasizes the importance of using hyper‐resolution versus coarse‐resolution modeling in areas characterized by complex terrain The study emphasizes the importance of an accurate hyper‐resolution precipitation product used to drive model simulations

The accurate representation of the local‐scale variability of precipitation plays an important role in understanding the hydrological cycle and land‐atmosphere interactions in the High Mountain Asia region. Therefore, the development of hyper‐resolution precipitation data is of urgent need. In this study, we propose a statistical framework to downscale the Modern‐Era Retrospective Analysis for Research and Applications, Version 2 (MERRA‐2) precipitation product using the random forest classification and regression algorithm. A set of variables representing atmospheric, geographic, and vegetation cover information are selected as model predictors, based on a recursive feature elimination method. The downscaled precipitation product is validated in terms of magnitude and variability against a set of ground‐ and satellite‐based observations. Results suggest improvements with respect to the original resolution MERRA‐2 precipitation product and comparable performance with gauge‐adjusted satellite precipitation products. This is the first use of recursive feature elimination in predictor selection for spatial downscaling of precipitationRandom forest classification is applied to create high‐resolution precipitation mask to identify whether the pixels are rainy or notValidation is performed against ground‐based precipitation observations and remote sensing precipitation products over High Mountain Asia

The accuracy of forcing data greatly impacts the ability of land surface models (LSMs) to produce realistic simulations of land surface processes. With this in mind, the multi‐institutional North American Land Data Assimilation System (NLDAS) project has produced retrospective (1996–2002) and real‐time (1999–present) data sets to support its LSM modeling activities. Featuring 0.125° spatial resolution, hourly temporal resolution, nine primary forcing fields, and six secondary validation/model development fields, each data set is based on a backbone of Eta Data Assimilation System/Eta data and is supplemented with observation‐based precipitation and radiation data. Hourly observation‐based precipitation data are derived from a combination of daily National Center for Environmental Prediction Climate Prediction Center (CPC) gauge‐based precipitation analyses and hourly National Weather Service Doppler radar‐based (WSR‐88D) precipitation analyses, wherein the hourly radar‐based analyses are used to temporally disaggregate the daily CPC analyses. NLDAS observation‐based shortwave values are derived from Geostationary Operational Environmental Satellite radiation data processed at the University of Maryland and at the National Environmental Satellite Data and Information Service. Extensive quality control and validation efforts have been conducted on the NLDAS forcing data sets, and favorable comparisons have taken place with Oklahoma Mesonet, Atmospheric Radiation Measurement Program/cloud and radiation test bed, and Surface Radiation observation data. The real‐time forcing data set is constantly evolving to make use of the latest advances in forcing‐related data sets, and all of the real‐time and retrospective data are available online at http://ldas.gsfc.nasa.govfor visualization and downloading in both full and subset forms.

Atmospheric forcing used by land surface models is a critical component of the North American Land Data Assimilation System (NLDAS) and its quality crucially affects the final product of NLDAS and our work on model improvement. A three‐year (September 1996–September 1999) retrospective forcing data set was created from the Eta Data Assimilation System and observations and used to run the NLDAS land surface models for this period. We compared gridded NLDAS forcing with station observations obtained from networks including the Oklahoma Mesonet and Atmospheric Radiation Measurement/Cloud and Radiation Testbed at the southern Great Plains. Differences in all forcing variables except precipitation between the NLDAS forcing data set and station observations are small at all timescales. While precipitation data do not agree very well at an hourly timescale, they do agree better at longer timescales because of the way NLDAS precipitation forcing is generated. A small high bias in downward solar radiation and a low bias in downward longwave radiation exist in the retrospective forcing. To investigate the impact of these differences on land surface modeling we compared two sets of model simulations, one forced by the standard NLDAS product and one with station‐observed meteorology. The differences in the resulting simulations of soil moisture and soil temperature for each model were small, much smaller than the differences between the models and between the models and observations. This indicates that NLDAS retrospective forcing provides an excellent state‐of‐the‐art data set for land surface modeling, at least over the southern Great Plains region.

This is the second part of a study on the cold season process modeling in the North American Land Data Assimilation System (NLDAS). The first part concentrates on the assessment of model simulated snow cover extent. In this second part, the focus is on the evaluation of simulated snow water equivalent (SWE) from the four land surface models (Noah, MOSAIC, SAC and VIC) in the NLDAS. Comparisons are made with observational data from the Natural Resources Conservation Service's Snowpack Telemetry (SNOTEL) network for a 3‐year retrospective period at selected sites in the mountainous regions of the western United States. All models show systematic low bias in the maximum annual simulated SWE that is most notable in the Cascade and Sierra Nevada regions where differences can approach 1000 mm. Comparison of NLDAS precipitation forcing with SNOTEL measurements revealed a large bias in the NLDAS annual precipitation which may be lower than the SNOTEL record by up to 2000 mm at certain stations. Experiments with the VIC model indicated that most of the bias in SWE is removed by scaling the precipitation by a regional factor based on the regression of the NLDAS and SNOTEL precipitation. Individual station errors may be reduced further still using precipitation scaled to the local station SNOTEL record. Furthermore, the NLDAS air temperature is shown to be generally colder in winter months and biased warmer in spring and summer when compared to the SNOTEL record, although the level of bias is regionally dependent. Detailed analysis at a selected station indicate that errors in the air temperature forcing may cause the partitioning of precipitation into snowfall and rainfall by the models to be incorrect and thus may explain some of the remaining errors in the simulated SWE.

In support of the World Climate Research Program GEWEX Continental‐Scale International Project (GCIP) and the GEWEX Americas Prediction Project (GAPP), real‐time estimates of shortwave radiative fluxes, both at the surface and at the top of the atmosphere, are being produced operationally by the National Oceanic and Atmospheric Administration (NOAA)/National Environmental Satellite Data and Information Service using observations from GOES images. The inference scheme has been developed at the Department of Meteorology, University of Maryland, and the atmospheric and surface model input parameters are produced and provided by the NOAA/National Centers for Environmental Prediction. The radiative fluxes are being evaluated on hourly, daily, and monthly timescales using observations at about 50 stations. The satellite estimates have been found to be within acceptable limits during snow‐free periods, but the difficulty in detecting clouds over snow affects the accuracy during the winter season. In what follows, this activity is discussed, and evaluation results of the derived fluxes against ground observations for time periods of 1–2 years are presented.

This study evaluates the cold season process modeling in the North American Land Data Assimilation System (NLDAS) and consists of two parts: (1) assessment of land surface model simulations of snow cover extent and (2) evaluation of snow water equivalent. In this first part, simulations of snow cover extent from the four land surface models (Noah, MOSAIC, Sacramento land surface model (SAC), and variable infiltration capacity land surface model (VIC)) in the NLDAS were compared with observational data from the Interactive Multisensor Snow and Ice Mapping System for a 3 year retrospective period over the conterminous United States. In general, all models simulate reasonably well the regional‐scale spatial and seasonal dynamics of snow cover. Systematic biases are seen in the model simulations, with consistent underestimation of snow cover extent by MOSAIC (−19.8% average bias) and Noah (−22.5%), and overestimation by VIC (22.3%), with SAC being essentially unbiased on average. However, the level of bias at the regional scale varies with geographic location and elevation variability. Larger discrepancies are seen over higher elevation regions of the northwest of the United States that may be due, in part, to errors in the meteorological forcings and also at the snow line boundary, where most temporal and spatial variability in snow cover extent is likely to occur. The spread between model simulations is fairly low and generally envelopes the observed data at the mean regional scale, indicating that the models are quite capable of simulating the general behavior of snow processes at these scales. Intermodel differences can be explained to some extent by differences in the model representations of subgrid variability and parameterizations of snow cover extent.

North American Land Data Assimilation System (NLDAS) land surface models have been run for a retrospective period forced by atmospheric observations from the Eta analysis and actual precipitation and downward solar radiation to calculate land hydrology. We evaluated these simulations using in situ observations over the southern Great Plains for the periods of May–September of 1998 and 1999 by comparing the model outputs with surface latent, sensible, and ground heat fluxes at 24 Atmospheric Radiation Measurement/Cloud and Radiation Testbed stations and with soil temperature and soil moisture observations at 72 Oklahoma Mesonet stations. The standard NLDAS models do a fairly good job but with differences in the surface energy partition and in soil moisture between models and observations and among models during the summer, while they agree quite well on the soil temperature simulations. To investigate why, we performed a series of experiments accounting for differences between model‐specified soil types and vegetation and those observed at the stations, and differences in model treatment of different soil types, vegetation properties, canopy resistance, soil column depth, rooting depth, root density, snow‐free albedo, infiltration, aerodynamic resistance, and soil thermal diffusivity. The diagnosis and model enhancements demonstrate how the models can be improved so that they can be used in actual data assimilation mode.

The process of a model adjusting to its forcing (model spin‐up) can severely bias land surface simulations, and result in questionable land surface model (LSM) output during the spin‐up process. To gain a better understanding of how spin‐up processes affect complex spatial and temporal land surface modeling situations in general, and the Retrospective North American Land Data Assimilation System (NLDAS) simulations in particular, a two‐phase study was conducted. The first phase examined results from Control, Wet, and Dry 11 year‐long Mosaic simulations, while the second phase attempted to explain spin‐up behavior in NLDAS Retrospective simulations from the Mosaic, Noah, VIC and Sacramento LSMs based in part on the results from phase 1. Total column and root zone soil moisture spin up slowly, while evaporation and deep soil temperature spin up more quickly. Mosaic soil moisture initialization with NCEP/DOE Global Reanalysis 2 (NCEP/DOE R‐2) data (Control run) leads to a faster spin‐up time than saturated (Wet run) or dry (Dry run) initialization, with the Control run reaching equilibrium 1 to 2 years sooner than the Wet run and 3 to 4 years more quickly than the Dry run. Overall, practical drift of land surface stores and output ceased in the Control run within approximately 1 year, and fine‐scale equilibrium was reached within 5.5 years. Spin‐up times exhibited large spatial variability, and although no single causal factor could be determined, they were correlated most strongly with precipitation and temperature forcing. In general, NLDAS models reach a state of rough equilibrium within the first 1 to 2 years of the 3‐year Retrospective simulation. The Sacramento LSM has the shortest spin‐up phase, followed by the Mosaic, VIC, and Noah LSMs. Initial NCEP/DOE R‐2 conditions were too dry in general for the VIC and Noah LSMs, and too moist for the Mosaic and Sacramento LSMs. These results indicate that in most cases, the 1‐year spin‐up time used in the Retrospective NLDAS simulations eliminated spin‐up problems from the subsequent period that was used for analysis.

Presents a photograph on Carhartt-clad twins of photographer Paul Houser in his family's home in Matanuska Valley, Alaska.