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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. Key Points: 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 [ABSTRACT FROM AUTHOR]

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. Key Points: 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 [ABSTRACT FROM AUTHOR]

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. Key Points: 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 [ABSTRACT FROM AUTHOR]

Recognition of short linear motifs (SLiMs) or peptides by proteins is an important component of many cellular processes. However, due to limited and degenerate binding motifs, prediction of cellular targets is challenging. In addition, many of these interactions are transient and of relatively low affinity. Here, we focus on one of the largest families of SLiM‐binding domains in the human proteome, the PDZ domain. These domains bind the extreme C‐terminus of target proteins, and are involved in many signaling and trafficking pathways. To predict endogenous targets of PDZ domains, we developed MotifAnalyzer‐PDZ, a program that filters and compares all motif‐satisfying sequences in any publicly available proteome. This approach enables us to determine possible PDZ binding targets in humans and other organisms. Using this program, we predicted and biochemically tested novel human PDZ targets by looking for strong sequence conservation in evolution. We also identified three C‐terminal sequences in choanoflagellates that bind a choanoflagellate PDZ domain, the Monsiga brevicollis SHANK1 PDZ domain (mbSHANK1), with endogenously‐relevant affinities, despite a lack of conservation with the targets of a homologous human PDZ domain, SHANK1. All three are predicted to be signaling proteins, with strong sequence homology to cytosolic and receptor tyrosine kinases. Finally, we analyzed and compared the positional amino acid enrichments in PDZ motif‐satisfying sequences from over a dozen organisms. Overall, MotifAnalyzer‐PDZ is a versatile program to investigate potential PDZ interactions. This proof‐of‐concept work is poised to enable similar types of analyses for other SLiM‐binding domains (e.g., MotifAnalyzer‐Kinase). MotifAnalyzer‐PDZ is available at http://motifAnalyzerPDZ.cs.wwu.edu. [ABSTRACT FROM AUTHOR]

This study examines the land-atmosphere coupling strength during the 2004 North American monsoon ( NAM) using the ΔΩ index from ensemble model simulations. The strength of coupling varies geographically over different monsoon phases. The coupling strength over the entire monsoon period is primarily attributed to the monsoon mature phase with strong signal of coupling in the eastern NAM region. A decomposition of the land-atmosphere coupling suggests that land segment contributes to the strong coupling during the monsoon onset, mature phase and the overall monsoon season, while the atmospheric segment dominates the strong coupling during the decay phase. [ABSTRACT FROM AUTHOR]

The article discusses objectives of and the rationale behind developing HyperHydro network, which is an open network of scientists for simulating large-scale terrestrial hydrology and water resources. Topics discussed include global environmental change that require hyper-resolution descriptions of terrestrial water resources, hydrological models to support water resources management and significant challenges in providing hydrological information globally.

This study examined potential seasonal predictability of precipitation and 2-m temperature in the recent global reanalyses from 1979 to 2012. The reanalyses being investigated are R1, R2, ERA-40, JRA25, ERA-I, MERRA, CFSR and 20CR. When compared against Global Precipitation Climatology Project ( GPCP) precipitation, ERA-I and CFSR provide the best estimates of potential predictability, MERRA, R1 and R2 overestimate predictability and ERA-40 and JRA25 are unrealistic in the tropics. Predictability estimates of 2-m temperature from 20CR, JRA25, R1, MERRA, CFSR and R2 exhibit better agreement with the reanalysis ensemble mean than ERA-40 and ERA-I, which identify less predictability. [ABSTRACT FROM AUTHOR]

Eleven years of daily 500 m gridded Terra Moderate Resolution Imaging Spectroradiometer (MODIS) (MOD10A1) snow cover fraction (SCF) data are evaluated in terms of snow presence detection in Colorado and Washington states. The SCF detection validation study is performed using in-situ measurements and expressed in terms of snow and land detection and misclassification frequencies. A major aspect addressed in this study involves the shifting of pixel values in time due to sensor viewing angles and gridding artifacts of MODIS sensor products. To account for this error, 500 m gridded pixels are grouped and aggregated to different-sized areas to incorporate neighboring pixel information. With pixel aggregation, both the probability of detection (POD) and the false alarm ratios increase for almost all cases. Of the false negative (FN) and false positive values (referred to as the total error when combined), FN estimates dominate most of the total error and are greatly reduced with aggregation. The greatest POD increases and total error reductions occur with going from a single 500 m pixel to 3×3-pixel averaged areas. Since the MODIS SCF algorithm was developed under ideal conditions, SCF detection is also evaluated for varying conditions of vegetation, elevation, cloud cover and air temperature. Finally, using a direct insertion data assimilation approach, pixel averaged MODIS SCF observations are shown to improve modeled snowpack conditions over the single pixel observations due to the smoothing of more error-prone observations and more accurately snow-classified pixels. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Monitoring Earth's terrestrial water conditions is critically important to many hydrological applications such as global food production; assessing water resources sustainability; and flood, drought, and climate change prediction. These needs have motivated the development of pilot monitoring and prediction systems for terrestrial hydrologic and vegetative states, but to date only at the rather coarse spatial resolutions (∼10-100 km) over continental to global domains. Adequately addressing critical water cycle science questions and applications requires systems that are implemented globally at much higher resolutions, on the order of 1 km, resolutions referred to as hyperresolution in the context of global land surface models. This opinion paper sets forth the needs and benefits for a system that would monitor and predict the Earth's terrestrial water, energy, and biogeochemical cycles. We discuss six major challenges in developing a system: improved representation of surface-subsurface interactions due to fine-scale topography and vegetation; improved representation of land-atmospheric interactions and resulting spatial information on soil moisture and evapotranspiration; inclusion of water quality as part of the biogeochemical cycle; representation of human impacts from water management; utilizing massively parallel computer systems and recent computational advances in solving hyperresolution models that will have up to 109 unknowns; and developing the required in situ and remote sensing global data sets. We deem the development of a global hyperresolution model for monitoring the terrestrial water, energy, and biogeochemical cycles a 'grand challenge' to the community, and we call upon the international hydrologic community and the hydrological science support infrastructure to endorse the effort. [ABSTRACT FROM AUTHOR]

Land surface models are typically constrained by one or a few observed variables, while assuming that the internal water and energy partitioning is sensitive to those observed variables and realistic enough to simulate unobserved variables. To verify these assumptions, in situ soil climate analysis network (SCAN) observations in the Lower Mississippi Basin (2002-2008) are analysed to quantify water and energy budget components and they are compared to Community Land Model (CLM3·5) simulations. The local soil texture is identified as a major indicator for water storage characteristics and the Normalized Difference Vegetation Index shows potential as a drought indicator in summer months. Both observations and simulations indicate a regime where, except in some summer months, evapotranspiration controls soil moisture. CLM simulations with different soil texture assignments show discharge sensitivity to soil moisture, but almost no impact on evapotranspiration and other energy balance components. The observed and simulated water budgets show a similar partitioning. However, the SCAN observed water balance does not close because of precipitation measurement errors, unobserved irrigation, lack of specific storage change measurements and errors in the computed actual evapotranspiration. The simulated heat flux partitioning differs from that 'observed', with a larger (resp. smaller) fraction of net radiation being used by latent (resp. sensible) heat flux, and unobserved freeze and thaw events. The comparison between observations and model simulations suggests that a consistent observation collection for multiple variables would be needed to constrain and improve the full set of land surface variable estimates. Copyright © 2010 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Land surface models are usually biased in at least a subset of the simulated variables even after calibration. Bias estimation may therefore be needed for data assimilation. Here, in situ soil moisture profile observations in a small agricultural field were merged with Community Land Model (CLM2.0) simulations using different algorithms for state and forecast bias estimation with and without bias correction feedback. Simple state updating with the conventional ensemble Kalman filter (EnKF) allows for some implicit forecast bias correction. It is possible to estimate the soil moisture bias explicitly and derive superior soil moisture estimates with a generalized EnKF that uses a simple persistence model for the bias and assumes that the a priori bias error covariance is proportional to the a priori state error covariance. For the case of bi-weekly assimilation of the entire profile of soil moisture observations, bias estimation and correction typically reduces the RMSE in soil moisture (over the standard EnKF without bias correction) by around 60 percent. However, under the above assumptions, significant improvements are limited to state variables for which observations are available. Therefore, it is crucial to measure the state variables of interest. The best variant for state and bias estimation depends on the nature of the model bias and the output of interest to the user. In a model that is only biased for soil moisture, large and frequent increments for soil moisture updating may be required, which in turn may negatively impact the water balance and output fluxes. It is then better to post-process the soil moisture with the bias analysis without updating the model state. [ABSTRACT FROM AUTHOR]

An ensemble Kalman filter for state estimation and a bias estimation algorithm were applied to estimate individual soil moisture profiles in a small corn field with the CLM2.0 model through the assimilation of measurements from capacitance probes. Both without and with inclusion of bias correction, the effect of the assimilation frequency, the assimilation depth, and the number of observations assimilated per profile were studied. Assimilation of complete profiles had the highest impact on deeper soil layers, and the optimal assimilation frequency was about 1-2 weeks, if bias correction was applied. The optimal assimilation depth depended on the calibration results. Assimilation in the surface layer had typically less impact than assimilation in other layers. Through bias correction the soil moisture estimate greatly improved. In general, the correct propagation of the innovations for both the bias-blind state and bias filtering from any layer to other layers was insufficient. The approximate estimation of the a priori (bias) error covariance and the choice of a zero-initialized persistent bias model made it impossible to estimate the bias in layers for which no observations were available. [ABSTRACT FROM AUTHOR]

Uncertainties in assessing the effects of global-scale perturbations on the climate system arise primarily from an inadequate understanding of the hydrological cycle: on land, in oceans, and in the atmosphere and biosphere. Because of this uncertainty, almost all science-based initiatives have expressed the need for continued advances in global observations and modeling of the Earth system. It is in this spirit that we advocate establishing a hydrologic remote sensing observatory (RSO) to advance sensing technologies and their use in scientific inquiry into hydrologic processes. There are two fundamental reasons why establishing such a RSO is timely. The first is operational: Developing assimilation techniques to estimate unobserved fluxes and uncertainties in hydrologic forecasts has sufficiently matured to take advantage of computing facilities and detailed hydrologic observations shaped by the RSO. The second is scientific: This RSO will permit us to refine knowledge from physical and hydrologic models that can then be converted to local and global strategies for water resources management and ecosystem health evaluation. The authors outline the conceptual design, scope, and functionality of a RSO and present four examples to illustrate how the hydrologic community can take advantage of such facility. [ABSTRACT FROM AUTHOR]

Examines the use of OhmMapper Instrument in measuring the soil moisture content in the root zone. Employment of electrical resistance measurements to infer soil moisture; Spatial distribution of soil moisture content; Validation of soil moisture modeling.

The feasibility of synthesizing distributed fields of soil moisture by the novel application of four-dimensional data assimilation (4DDA) applied in a hydrological model is explored. Six 160-km2 push broom microwave radiometer (PBMR) images gathered over the Walnut Gulch experimental watershed in southeast Arizona were assimilated into the Topmodel-based Land-Atmosphere Transfer Scheme (TOPLATS) using several alternative assimilation procedures. Modification of traditional assimilation methods was required to use these high-density PBMR observations. The images were found to contain horizontal correlations that imply length scales of several tens of kilometers, thus allowing information to be advected beyond the area of the image. Information on surface soil moisture also was assimilated into the subsurface using knowledge of the surface- subsurface correlation. Newtonian nudging assimilation procedures are preferable to other techniques because they nearly preserve the observed patterns within the sampled region but also yield plausible patterns in unmeasured regions and allow information to be advected in time. [ABSTRACT FROM AUTHOR]

The article presents information on various bulletins. Some of these are as, "Louisiana's Human Resources. Number, Distribution, and Composition of the Population, 1960," by Alvin L. Bertrand and "Louisiana's Human Resources. Agribusiness and the Labor Force," by Arthur R. Jones and Lee Taylor. These bulletins, based on the 1960 Decennial Census, present interesting and valuable observations about the characteristics of the people of Louisiana. Bertrand reports "basic general information on the number, distribution, and composition of the population" and points out some of their significant implications for future planning and developmental action. His major findings may be summarized as follows: (1) between 1950-60, Louisiana had a relatively rapid rate of growth, 21.4 percent, compared with the nation; (2) urban areas of the state gained population and rural places generally lost; (3) women outnumbered men, sex ratios for 1950 and 1960 being 96.7 and 95.6, respectively; (4) in 1960 the state had the highest percentage of nonwhite in the nation, except for Mississippi and South Carolina; (5) a persistent high birthrate, since 1940, caused a relatively young population for the state in 1960; and (6) although the level of income and education in 1960 was remarkably higher than those in 1950, the state had relatively low level of income and education compared with other states.

One of the chief findings of the study is that the library system reached ten percent of the population during its first year of operation, which is approximately the user rate of public libraries for the nation. This record was achieved through careful location of bookmobile stops in outlying districts. Library users both accept and reject the service more that nonusers and have higher levels of living. The accepter group, compared with the rejecter, has lower levels of living, and as expected, makes greater use of facilities offered. These results are based on responses from 1,604 individuals and 557 households in nonmetropolitan areas of the county. Those to be interviewed were selected by area sampling procedures and interviewed by volunteer workers and staff members. The general objective of the library study was to supply basic information for use by county library planners. [ABSTRACT FROM AUTHOR]

The article presents the response to the comment made by Keith J. Beven and Hannah L. Cloke in the study "Hyperresolution global land surface modeling: Meeting a grand challenge for monitoring Earth's terrestrial water." It states that the commentary is just focused on one challenge or on the building block described. It also says that the Grand Challenge interpretation is not the purpose of the study. It adds that the study addresses that epistemic uncertainty is pursued under scale issues.