Empirical Mode Decomposition (EMD) is used to examine the relationship between precipitation and surface temperature from six regions. Three regions are defined by physiography: world, ocean, and land. The other three regions are defined by averaged precipitation: dry, normal and wet. Monthly averaged daily precipitation rate from the Global Precipitation Climatology Project are compared with average monthly surface air temperature anomalies from the Goddard Institute for Space Studies using EMD. The EMD process produces component time series referred to as intrinsic mode functions (IMFs). Theses IMFs are ordered by frequency from high to low. Eight IMFs were produced for each the time series. The first three IMFs corresponded to seasonal, semi-annual and annual variations, respectively. IMF 4 to 6 corresponded to a biennial, pentennial and decadal climate signals, respectively. IMF 7 was related to the broad 20-30 year period, with the trend being revealed in IMF 8. The time series spanned the period from January 1980 to December 2015 at monthly intervals. Temperature and precipitation time series from six sampling regions were analyzed for evidence of correlation. Results from the analysis reveal the following: (1) The EMD process reveals both linear and non-linear trends. The trends are not entirely consistent between regions though they are highly correlated. (2) Apparent wave-to-wave interactions between high and low frequency components appear to be observed in the IMF 1 and 2. These distortions appear to correspond to the troughs and peaks in the decadal cycle captured in IMF 6 and may related to the solar cycle. (3) The correlation between precipitation and temperature increases with increasing IMF number.

Existing global FT records, derived from the Soil Moisture Active Passive (SMAP), the Advanced Scanning Microwave Radiometer (AMSR), and the Special Sensor Microwave Imager (SSM/I) produce relatively course spatial resolution (25-36km) binary FT classifications. These classifications can vary widely depending on the microwave bands used, topography, and land cover, leading to a somewhat ambiguous definition of ‘frozen’ and ‘thawed’ states. In this study, we assess the relationship between satellite observation derived FT products over North America compared to modeled near-surface temperatures and land surface temperature (LST) from the Geostationary Operational Environmental Satellite system (GOES). Utilizing the higher spatial resolution of these products (~4.5km), sub-grid scale variability and its relationship to courser microwave FT classifications was assessed. Through an analysis of spatial variability and uncertainty across North America, five focus study pixels each representing unique FT profiles were examined. These included pixels in: (1) Southern Plains (36, -97), (2) Tundra (61, -76), (3) Northern Forest (47, -74), (4) Northern Plains (52, -103), and Mountainous (38.9, -107.9). The model ensemble adequately captured near surface temperatures as they relate to FT classifications in Tundra, the Northern Plains, and Northern Forest regions. On average, 85.3% to 99.6% of sub-grid cells were below freezing when FT products classified the associated pixels as frozen. GOES - LST observations were shown to have the highest proportion of sub-grid cells below freezing on average, when classified as frozen by FT products (97.3% - 100%) across the same 3 focus locations. However, we also find that fractional FT products utilizing higher resolution data inputs, such as LST, would provide a considerable improvement in mountainous regions with high inter-grid cell heterogeneity, in regions characterized by ephemeral FT events (Southern Plains), as well as during freeze and thaw onset periods. These locations showed a significant reduction in the average temperature product frozen proportion associated with frozen classifications (as low as 5.8%). This study provides insight to improving representation of FT state and providing a clearer meaning of what constitutes a ‘frozen’ classification.

In this study, we have developed a hyper-resolution land-surface forcing dataset (temperature, pressure, humidity, wind speed, incident longwave and shortwave radiation) from coarse resolution products using a physically-based downscaling approach. These downscaling techniques rely on correlations with landscape variables, such as topography, temperature lapse rate corrections, surface roughness and land cover. A proof-of-concept has been implemented over the Oklahoma domain, where high-resolution observations are available for validation purposes. The hourly NLDAS (North America Land Data Assimilation System) forcing data at 0.125° have been downscaled to 500m resolution over the study area during 2015. Results show that correlation coefficients between the downscaled forcing dataset and ground observations are consistently higher and biases are lower than the ones between the NLDAS forcing dataset at their native resolution and ground observations. Results are therefore encouraging as they demonstrate that the 500m forcing dataset has a good agreement with the ground information and can be adopted to force the land surface model for land state estimation. The Noah-MP land surface model is then forced with both the native resolution NLDAS dataset and the downscaled one to simulate surface and root zone soil moisture. Model outputs are compared with in situ soil moisture observations and SMAP (Soil Moisture Active Passive Mission) products at different spatial resolutions. This work will result in a radical improvement over the current state-of-the-art forcing data and will move into the era of hyper-resolution land modeling.

The Snow Ensemble Uncertainty Project (SEUP) is an effort to establish a baseline characterization of snow water equivalent (SWE) uncertainty across North America with the goal of informing global snow observational needs. An ensemble-based modeling approach, encompassing a suite of current operational models, is used to assess the uncertainty in SWE and total snow storage (SWS) estimation during the 2009-2017 period. The highest modeled SWE uncertainty is observed in mountainous regions, likely due to the relatively deep snow, forcing uncertainties, and variability between the different models in resolving the snow processes over complex terrain. This highlights a need for high-resolution observations in mountains to capture the high spatial SWE variability. The greatest SWS is found in Tundra regions where, even though the spatiotemporal variability in modeled SWE is low, there is considerable uncertainty in the SWS estimates due to the large areal extent over which those estimates are spread. This highlights the need for high accuracy in snow estimations across the Tundra. In mid-latitude boreal forests, large uncertainties in both SWE and SWS indicate that vegetation-snow impacts are a critical area where focused improvements to modeled snow estimation efforts need to be made. Finally, the SEUP results indicate that SWE uncertainty is driving runoff uncertainty and measurements may be beneficial in reducing uncertainty in SWE and runoff, during the melt season at high latitudes (e.g., Tundra and Taiga regions) and in the Western mountain regions, whereas observations at (or near) peak SWE accumulation are more helpful over the mid-latitudes.

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 first paper of the two-part series focuses on demonstrating the predictability 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 degree (∼ 1-km) and 0.25 degree (∼ 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 degree 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 predictability 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.