Atmosphere–Land Surface Interactions over the Southern Great Plains: Characterization from Pentad Analysis of DOE ARM Field Observations and NARR

2013 ◽  
Vol 26 (3) ◽  
pp. 875-886 ◽  
Author(s):  
Alfredo Ruiz-Barradas ◽  
Sumant Nigam
Keyword(s):  
Soil Moisture ◽  
Water Balance ◽  
Great Plains ◽  
Land Surface ◽  
Precipitation Variability ◽  
Surface Interactions ◽  
Department Of Energy ◽  
Surface Radiation ◽  
Shortwave Radiation ◽  
Southern Great Plains

Abstract The Department of Energy Atmospheric Radiation Measurement Program (ARM) Southern Great Plains (SGP) site data are analyzed to provide insight into atmosphere–land surface interactions generating summertime precipitation variability. Pentad-averaged (5 days) data are analyzed; the average is long enough to suppress synoptic variability but sufficiently short to resolve atmosphere–land surface interactions. Intercomparison with the precipitation-assimilating North American Regional Reanalysis (NARR) helps with in-depth investigation of the processes. The analysis seeks to ascertain the process sequence, especially the role of evapotranspiration and soil-moisture–radiation feedbacks in the generation of regional precipitation variability at this temporal scale. Transported moisture dominates over evapotranspiration in precipitation variability over the region, from both magnitude of the contribution to regional water balance and its apparent temporal lead at pentad resolution. Antecedent and contemporaneous evapotranspiration are found to be negatively correlated with precipitation, albeit statistically insignificant; only lagging correlations are positive, peaking at 2-pentad lag following precipitation, substantiating the authors’ characterization of the water balance over SGP, and extending the authors’ previous findings on the dominance of moisture flux convergence in generating precipitation variability at monthly scales. Precipitation episodes are linked with net negative surface radiation anomalies (i.e., with an energy-deprived land surface state that cannot fuel evapotranspiration), ruling out radiatively driven positive feedback on precipitation. Although the net longwave signal is positive because of a colder land surface (less upward terrestrial radiation), it is more than offset by the cloudiness-related reduction in downward shortwave radiation. Thus, ARM (NARR) data do not support the soil-moisture–precipitation feedback hypothesis over the SGP at pentad time scales; however, it may work at subpentad resolution and over other regions.

scholarly journals Summertime Continental Shallow Cumulus Cloud Detection Using GOES-16 Satellite and Ground-Based Stereo Cameras at the DOE ARM Southern Great Plains Site

2021 ◽  
Vol 13 (12) ◽  
pp. 2309
Author(s):  
Jingjing Tian ◽  
Yunyan Zhang ◽  
Stephen A. Klein ◽  
Likun Wang ◽  
Rusen Öktem ◽  
...  
Keyword(s):  
Great Plains ◽  
Land Surface ◽  
Detection Threshold ◽  
Department Of Energy ◽  
Surface Reflectance ◽  
Cloud Detection ◽  
Southern Great Plains ◽  
Clear Sky ◽  
Shallow Cumulus ◽  
Stereo Cameras

Summertime continental shallow cumulus clouds (ShCu) are detected using Geostationary Operational Environmental Satellite (GOES)-16 reflectance data, with cross-validation by observations from ground-based stereo cameras at the Department of Energy Atmospheric Radiation Measurement Southern Great Plains site. A ShCu cloudy pixel is identified when the GOES reflectance exceeds the clear-sky surface reflectance by a reflectance detection threshold of ShCu, ΔR. We firstly construct diurnally varying clear-sky surface reflectance maps and then estimate the ∆R. A GOES simulator is designed, projecting the clouds reconstructed by stereo cameras towards the surface along the satellite’s slanted viewing direction. The dynamic ShCu detection threshold ΔR is determined by making the GOES cloud fraction (CF) equal to the CF from the GOES simulator. Although there are temporal variabilities in ΔR, cloud fractions and cloud size distributions can be well reproduced using a constant ΔR value of 0.045. The method presented in this study enables daytime ShCu detection, which is usually falsely reported as clear sky in the GOES-16 cloud mask data product. Using this method, a new ShCu dataset can be generated to bridge the observational gap in detecting ShCu, which may transition into deep precipitating clouds, and to facilitate further studies on ShCu development over heterogenous land surface.

scholarly journals Mechanisms of Seasonal Soil Moisture Drought Onset and Termination in the Southern Great Plains

2019 ◽  
Vol 20 (4) ◽  
pp. 751-771 ◽  
Author(s):  
Richard Seager ◽  
Jennifer Nakamura ◽  
Mingfang Ting
Keyword(s):  
Soil Moisture ◽  
North America ◽  
Great Plains ◽  
Land Surface ◽  
Tropical Pacific ◽  
Moisture Change ◽  
Southern Plains ◽  
Southern Great Plains ◽  
Continental Scale ◽  
Over Time

AbstractMechanisms of drought onset and termination are examined across North America with a focus on the southern Plains using data from land surface models and regional and global reanalyses for 1979–2017. Continental-scale analysis of covarying patterns reveals a tight coupling between soil moisture change over time and intervening precipitation anomalies. The southern Great Plains are a geographic center of patterns of hydrologic change. Drying is induced by atmospheric wave trains that span the Pacific and North America and place northerly flow anomalies above the southern Plains. In the southern Plains winter is least likely, and fall most likely, for drought onset and spring is least likely, and fall or summer most likely, for drought termination. Southern Plains soil moisture itself, which integrates precipitation over time, has a clear relationship to tropical Pacific sea surface temperature (SST) anomalies with cold conditions favoring dry soils. Soil moisture change, however, though clearly driven by precipitation, has a weaker relation to SSTs and a strong relation to internal atmospheric variability. Little evidence is found of connection of drought onset and termination to driving by temperature anomalies. An analysis of particular drought onsets and terminations on the seasonal time scale reveals commonalities in terms of circulation and moisture transport anomalies over the southern Plains but a variety of ways in which these are connected into the large-scale atmosphere and ocean state. Some onsets are likely to be quite predictable due to forcing by cold tropical Pacific SSTs (e.g., fall 2010). Other onsets and all terminations are likely not predictable in terms of ocean conditions.

Pentad Evolution of the 1988 Drought and 1993 Flood over the Great Plains: An NARR Perspective on the Atmospheric and Terrestrial Water Balance

2009 ◽  
Vol 22 (20) ◽  
pp. 5366-5384 ◽  
Author(s):  
Scott J. Weaver ◽  
Alfredo Ruiz-Barradas ◽  
Sumant Nigam
Keyword(s):  
Soil Moisture ◽  
Water Balance ◽  
Great Plains ◽  
Land Surface ◽  
Surface States ◽  
Moisture Flux ◽  
Summer Drought ◽  
Southern Plains ◽  
The North ◽  
Terrestrial Water

Abstract The evolution of the atmospheric and land surface states during extreme hydroclimate episodes over North America is investigated using the North American Regional Reanalysis (NARR), which additionally, and successfully, assimilates precipitation. The pentad-resolution portrayals of the atmospheric and terrestrial water balance over the U.S. Great Plains during the 1988 summer drought and the July 1993 floods are analyzed to provide insight into the operative mechanisms including regional circulation (e.g., the Great Plains low-level jet, or GPLLJ) and hydroclimate (e.g., precipitation, evaporation, soil moisture recharge, runoff). The submonthly (but supersynoptic time scale) fluctuations of the GPLLJ are found to be very influential, through related moisture transport and kinematic convergence (e.g., ∂υ/∂y), with the jet anomalies in the southern plains leading the northern precipitation and related moisture flux convergence, accounting for two-thirds of the dry and wet episode precipitation amplitude. The soil moisture influence on hydroclimate evolution is assessed to be marginal as evaporation anomalies are found to lag precipitation ones, a lead–lag not discernible at monthly resolution. The pentad analysis thus corroborates the authors’ earlier findings on the importance of transported moisture over local evaporation in Great Plains’ summer hydroclimate variability. The regional water budgets—atmospheric and terrestrial—are found to be substantially unbalanced, with the terrestrial imbalance being unacceptably large. Pentad analysis shows the atmospheric imbalance to arise from the sluggishness of the NARR evaporation, including its overestimation in wet periods. The larger terrestrial imbalance, on the other hand, has its origins in the striking unresponsiveness of the NARR’s runoff, which is underestimated in wet episodes. Finally, the influence of ENSO and North Atlantic Oscillation (NAO) variability on the GPLLJ is quantified during the wet episode, in view of the importance of moisture transports. It is shown that a significant portion (∼25%) of the GPLLJ anomaly (and downstream precipitation) is attributable to NAO and ENSO’s influence, and that this combined influence prolongs the wet episode beyond the period of the instigating GPLLJ.

Dependence of Land Surface Albedo on Solar Zenith Angle: Observations and Model Parameterization

2008 ◽  
Vol 47 (11) ◽  
pp. 2963-2982 ◽  
Author(s):  
Fanglin Yang ◽  
Kenneth Mitchell ◽  
Yu-Tai Hou ◽  
Yongjiu Dai ◽  
Xubin Zeng ◽  
...  
Keyword(s):  
Great Plains ◽  
Zenith Angle ◽  
Land Surface ◽  
Solar Zenith Angle ◽  
Surface Albedo ◽  
Geographical Location ◽  
Department Of Energy ◽  
Surface Radiation ◽  
Radiation Budget ◽  
Direct Beam

Abstract This study examines the dependence of surface albedo on solar zenith angle (SZA) over snow-free land surfaces using the intensive observations of surface shortwave fluxes made by the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) Program and the National Oceanic and Atmospheric Administration Surface Radiation Budget Network (SURFRAD) in 1997–2005. Results are used to evaluate the National Centers for Environmental Prediction (NCEP) Global Forecast Systems (GFS) parameterization and several new parameterizations derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) products. The influence of clouds on surface albedo and the albedo difference between morning and afternoon observations are also investigated. A new approach is taken to partition the observed upward flux so that the direct-beam and diffuse albedos can be separately computed. The study focused first on the ARM Southern Great Plains Central Facility site. It is found that the diffuse albedo prescribed in the NCEP GFS matched closely with the observations. The direct-beam albedo parameterized in the GFS is largely underestimated at all SZAs. The parameterizations derived from the MODIS product underestimated the direct-beam albedo at large SZAs and slightly overestimated it at small SZAs. Similar results are obtained from the analyses of observations at other stations. It is also found that the morning and afternoon dependencies of direct-beam albedo on SZA differ among the stations. Attempts are made to improve numerical model algorithms that parameterize the direct-beam albedo as a product of the direct-beam albedo at SZA = 60° (or the diffuse albedo), which varies with surface type or geographical location and/or season, and a function that depends only on SZA. A method is presented for computing the direct-beam albedos over these snow-free land points without referring to a particular land-cover classification scheme, which often differs from model to model.

scholarly journals Enhancing Model Skill by Assimilating SMOPS Blended Soil Moisture Product into Noah Land Surface Model

2015 ◽  
Vol 16 (2) ◽  
pp. 917-931 ◽  
Author(s):  
Jifu Yin ◽  
Xiwu Zhan ◽  
Youfei Zheng ◽  
Jicheng Liu ◽  
Li Fang ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Surface Radiation ◽  
Radiation Budget ◽  
Shortwave Radiation ◽  
Surface Model ◽  
Spatial Coverage ◽  
In Situ Observations ◽  
The Impact

Abstract Many studies that have assimilated remotely sensed soil moisture into land surface models have generally focused on retrievals from a single satellite sensor. However, few studies have evaluated the merits of assimilating ensemble products that are merged soil moisture retrievals from several different sensors. In this study, the assimilation of the Soil Moisture Operational Products System (SMOPS) blended soil moisture (SBSM) product, which is a combination of soil moisture products from WindSat, Advanced Scatterometer (ASCAT), and Soil Moisture and Ocean Salinity (SMOS) satellite sensors is examined. Using the ensemble Kalman filter (EnKF), a synthetic experiment is performed on the global domain at 25-km resolution to assess the impact of assimilating the SBSM product. The benefit of assimilating SBSM is assessed by comparing it with in situ observations from U.S. Department of Agriculture Soil Climate Analysis Network (SCAN) and the Surface Radiation Budget Network (SURFRAD). Time-averaged surface-layer soil moisture fields from SBSM have a higher spatial coverage and generally agree with model simulations in the global patterns of wet and dry regions. The impacts of assimilating SMOPS blended data on model soil moisture and soil temperature are evident in both sparsely and densely vegetated areas. Temporal correlations between in situ observations and net shortwave radiation and net longwave radiation are higher with assimilating SMOPS blended product than without the data assimilation.

scholarly journals Investigation of Large-Scale Atmospheric Moisture Budget and Land Surface Interactions over U.S. Southern Great Plains including for CLASIC (June 2007)

2012 ◽  
Vol 13 (6) ◽  
pp. 1719-1738 ◽  
Author(s):  
Peter J. Lamb ◽  
Diane H. Portis ◽  
Abraham Zangvil
Keyword(s):  
Great Plains ◽  
Land Surface ◽  
Large Scale ◽  
Limited Range ◽  
Surface Interactions ◽  
Moisture Budget ◽  
Atmospheric Moisture ◽  
Flux Divergence ◽  
Southern Great Plains ◽  
Atmospheric Moisture Budget

Abstract The atmospheric moisture budget and surface interactions for the southern Great Plains are evaluated for contrasting May–June periods (1998, 2002, 2006, and 2007) as background for the Cloud and Land Surface Interaction Campaign (CLASIC) of (wet) 7–30 June 2007. Budget components [flux divergence (MFD), storage change (dPW), and inflow (IF/A)] are estimated from North American Regional Reanalysis data. Precipitation (P) is calculated from NCEP daily gridded data, evapotranspiration (E) is obtained as moisture budget equation residual, and the recycling ratio (PE/P) is estimated using a new equation. Regional averages are presented for months and five daily P categories. Monthly budget results show that E and E − P are strongly positively related to P; E − P generally is positive and balanced by positive MFD that results from its horizontal velocity divergence component (HD, positive) exceeding its horizontal advection component (HA, negative). An exception is 2007 (CLASIC), when E − P and MFD are negative and supported primarily by negative HA. These overall monthly results characterize low P days (≤0.6 mm), including for nonanomalous 2007, but weaken as daily P approaches 4 mm. In contrast, for 4 < P ≤ 8 mm day−1 E − P and MFD are moderately negative and balanced largely by negative HD except in 2007 (negative HA). This overall pattern was accentuated (including for nonanomalous 2007) when daily P > 8 mm. Daily PE/P ratios are small and of limited range, with P category averages 0.15–0.19. Ratios for 2007 are above average only for daily P ≤ 4 mm. CLASIC wetness principally resulted from distinctive MFD characteristics. Solar radiation, soil moisture, and crop status/yield information document surface interactions.

scholarly journals Assessment of Remotely Sensed and Modelled Soil Moisture Data Products in the U.S. Southern Great Plains

2020 ◽  
Vol 12 (12) ◽  
pp. 2030
Author(s):  
Bo Jiang ◽  
Hongbo Su ◽  
Kai Liu ◽  
Shaohui Chen
Keyword(s):  
Soil Moisture ◽  
Great Plains ◽  
Land Surface ◽  
European Space Agency ◽  
Remotely Sensed ◽  
The Other ◽  
Short Term ◽  
Southern Great Plains ◽  
The U.S

Soil moisture (SM) plays a crucial role in the water and energy flux exchange between the atmosphere and the land surface. Remote sensing and modeling are two main approaches to obtain SM over a large-scale area. However, there is a big difference between them due to algorithm, spatial-temporal resolution, observation depth and measurement uncertainties. In this study, an assessment of the comparison of two state-of-the-art remotely sensed SM products, Soil Moisture Active Passive (SMAP) and European Space Agency Climate Change Initiative (ESACCI), and one land surface modeled dataset from the North American Land Data Assimilation System project phase 2 (NLDAS-2), were conducted using 17 permanent SM observation sites located in the Southern Great Plains (SGP) in the U.S. We first compared the daily mean SM of three products with in-situ measurements; then, we decompose the raw time series into a short-term seasonal part and anomaly by using a moving smooth window (35 days). In addition, we calculate the daily spatial difference between three products based on in-situ data and assess their temporal evolution. The results demonstrate that (1) in terms of temporal correlation R, the SMAP (R = 0.78) outperforms ESACCI (R = 0.62) and NLDAS-2 (R = 0.72) overall; (2) for the seasonal component, the correlation R of SMAP still outperforms the other two products, and the correlation R of ESACCI and NLDAS-2 have not improved like the SMAP; as for anomaly, there is no difference between the remotely sensed and modeling data, which implies the potential for the satellite products to capture the variations of short-term rainfall events; (3) the distribution pattern of spatial bias is different between the three products. For NLDAS-2, it is strongly dependent on precipitation; meanwhile, the spatial distribution of bias represents less correlation with the precipitation for two remotely sensed products, especially for the SMAP. Overall, the SMAP was superior to the other two products, especially when the SM was of low value. The difference between the remotely sensed and modeling products with respect to the vegetation type might be an important reason for the errors.

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