Diagnosis of Local Land–Atmosphere Feedbacks in India

2011 ◽  
Vol 24 (1) ◽  
pp. 251-266 ◽  
Author(s):  
O. A. Tuinenburg ◽  
R. W. A. Hutjes ◽  
C. M. J. Jacobs ◽  
P. Kabat
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
The United States ◽  
Monsoon Onset ◽  
Convective Precipitation ◽  
Onset Date ◽  
Positive Trend ◽  
Precipitation Trend ◽  
Atmospheric Conditions ◽  
The South

Abstract Following the convective triggering potential (CTP)–humidity index (HIlow) framework by Findell and Eltahir, the sensitivity of atmospheric convection to soil moisture conditions is studied for India. Using the same slab model as Findell and Eltahir, atmospheric conditions in which the land surface state affects convective precipitation are determined. For India, CTP–HIlow thresholds for land surface–atmosphere feedbacks are shown to be slightly different than for the United States. Using atmospheric sounding data from 1975 to 2009, the seasonal and spatial variations in feedback strength have been assessed. The patterns of feedback strengths thus obtained have been analyzed in relation to the monsoon timing. During the monsoon season, atmospheric conditions where soil moisture positively influences precipitation are present about 25% of the time. During onset and retreat of the monsoon, the south and east of India show more potential for feedbacks than the north. These feedbacks suggest that large-scale irrigation in the south and east may increase local precipitation. To test this, precipitation data (from 1960 to 2004) for the period about three weeks just before the monsoon onset date have been studied. A positive trend in the precipitation just before the monsoon onset is found for irrigated stations. It is shown that for irrigated stations, the trend in the precipitation just before the monsoon onset is positive for the period 1960–2004. For nonirrigated stations, there is no such upward trend in this period. The precipitation trend for irrigated areas might be due to a positive trend in the extent of irrigated areas, with land–atmosphere feedbacks inducing increased precipitation.

scholarly journals Soil moisture–precipitation coupling: observations from the Oklahoma Mesonet and underlying physical mechanisms

2015 ◽  
Vol 12 (3) ◽  
pp. 3205-3243 ◽  
Author(s):  
T. W. Ford ◽  
A. D. Rapp ◽  
S. M. Quiring ◽  
J. Blake
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Convective Available Potential Energy ◽  
The United States ◽  
Convective Precipitation ◽  
Significant Preference ◽  
Boundary Layer Height ◽  
Oklahoma Mesonet ◽  
Nexrad Radar ◽  
Positive Correlations

Abstract. Interactions between soil moisture and the atmosphere are driven by the partitioning of sensible and latent heating, through which, soil moisture has been connected to atmospheric modification that could potentially lead to initiation of convective precipitation. The majority of previous studies linking the land surface to subsequent precipitation have used atmospheric reanalysis or model datasets. In this study, we link in situ observations of soil moisture from more than 100 stations in Oklahoma to subsequent unorganized afternoon convective precipitation. We use hourly, high resolution NEXRAD radar-derived precipitation to identify convective events, and then compare the location of precipitation initiation to underlying soil moisture anomalies the morning prior. Overall we find a statistically significant preference for convective precipitation initiation over drier than normal soils, with over 70% of events initiating over soil moisture below the long-term median. The significant preference for precipitation initiation over drier than normal soils is in contrast with previous studies using satellite-based precipitation products to identify the region of maximum precipitation accumulation. We sub-sample 19 convective events occurring near Lamont, Oklahoma, where soundings of the atmospheric profile at 06:00 and 12:00 LST are also available. For these events, soil moisture is strongly, negatively correlated with the level of free convection, planetary boundary layer height, and surface temperature changes from 06:00 to 12:00 LST. We also find strong, positive correlations between morning soil moisture and morning-to-afternoon changes in convective available potential energy and convective inhibition. In general, the results of this study demonstrate that both positive and negative soil moisture feedbacks to the atmosphere are relevant in this region of the United States.

scholarly journals Improving Numerical Weather Predictions of Summertime Precipitation over the Southeastern United States through a High-Resolution Initialization of the Surface State

2011 ◽  
Vol 26 (6) ◽  
pp. 785-807 ◽  
Author(s):  
Jonathan L. Case ◽  
Sujay V. Kumar ◽  
Jayanthi Srikishen ◽  
Gary J. Jedlovec
Keyword(s):  
United States ◽  
Soil Moisture ◽  
High Resolution ◽  
Land Surface ◽  
Weather Prediction ◽  
Southeastern United States ◽  
Model Verification ◽  
Sea Surface ◽  
Convective Precipitation ◽  
Numerical Weather

Abstract It is hypothesized that high-resolution, accurate representations of surface properties such as soil moisture and sea surface temperature are necessary to improve simulations of summertime pulse-type convective precipitation in high-resolution models. This paper presents model verification results of a case study period from June to August 2008 over the southeastern United States using the Weather Research and Forecasting numerical weather prediction model. Experimental simulations initialized with high-resolution land surface fields from the National Aeronautics and Space Administration’s (NASA) Land Information System (LIS) and sea surface temperatures (SSTs) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) are compared to a set of control simulations initialized with interpolated fields from the National Centers for Environmental Prediction’s (NCEP) 12-km North American Mesoscale model. The LIS land surface and MODIS SSTs provide a more detailed surface initialization at a resolution comparable to the 4-km model grid spacing. Soil moisture from the LIS spinup run is shown to respond better to the extreme rainfall of Tropical Storm Fay in August 2008 over the Florida peninsula. The LIS has slightly lower errors and higher anomaly correlations in the top soil layer but exhibits a stronger dry bias in the root zone. The model sensitivity to the alternative surface initial conditions is examined for a sample case, showing that the LIS–MODIS data substantially impact surface and boundary layer properties. The Developmental Testbed Center’s Meteorological Evaluation Tools package is employed to produce verification statistics, including traditional gridded precipitation verification and output statistics from the Method for Object-Based Diagnostic Evaluation (MODE) tool. The LIS–MODIS initialization is found to produce small improvements in the skill scores of 1-h accumulated precipitation during the forecast hours of the peak diurnal convective cycle. Because there is very little union in time and space between the forecast and observed precipitation systems, results from the MODE object verification are examined to relax the stringency of traditional gridpoint precipitation verification. The MODE results indicate that the LIS–MODIS-initialized model runs increase the 10 mm h−1 matched object areas (“hits”) while simultaneously decreasing the unmatched object areas (“misses” plus “false alarms”) during most of the peak convective forecast hours, with statistically significant improvements of up to 5%. Simulated 1-h precipitation objects in the LIS–MODIS runs more closely resemble the observed objects, particularly at higher accumulation thresholds. Despite the small improvements, however, the overall low verification scores indicate that much uncertainty still exists in simulating the processes responsible for airmass-type convective precipitation systems in convection-allowing models.

scholarly journals Spatio–temporal Assessment of Drought in Ethiopia and the Impact of Recent Intense Droughts

2019 ◽  
Vol 11 (15) ◽  
pp. 1828 ◽  
Author(s):  
Yuei-An Liou ◽  
Getachew Mehabie Mulualem
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Pearson Correlation ◽  
Early Warning Systems ◽  
Heat Index ◽  
Trend Test ◽  
Northwestern Part ◽  
Precipitation Trend ◽  
Spatio Temporal ◽  
The Impact

The recent droughts that have occurred in different parts of Ethiopia are generally linked to fluctuations in atmospheric and ocean circulations. Understanding these large-scale phenomena that play a crucial role in vegetation productivity in Ethiopia is important. In view of this, several techniques and datasets were analyzed to study the spatio–temporal variability of vegetation in response to a changing climate. In this study, 18 years (2001–2018) of Moderate Resolution Imaging Spectroscopy (MODIS) Terra/Aqua, normalized difference vegetation index (NDVI), land surface temperature (LST), Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) daily precipitation, and the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS) soil moisture datasets were processed. Pixel-based Mann–Kendall trend analysis and the Vegetation Condition Index (VCI) were used to assess the drought patterns during the cropping season. Results indicate that the central highlands and northwestern part of Ethiopia, which have land cover dominated by cropland, had experienced decreasing precipitation and NDVI trends. About 52.8% of the pixels showed a decreasing precipitation trend, of which the significant decreasing trends focused on the central and low land areas. Also, 41.67% of the pixels showed a decreasing NDVI trend, especially in major parts of the northwestern region of Ethiopia. Based on the trend test and VCI analysis, significant countrywide droughts occurred during the El Niño 2009 and 2015 years. Furthermore, the Pearson correlation coefficient analysis assures that the low NDVI was mainly attributed to the low precipitation and water availability in the soils. This study provides valuable information in identifying the locations with the potential concern of drought and planning for immediate action of relief measures. Furthermore, this paper presents the results of the first attempt to apply a recently developed index, the Normalized Difference Latent Heat Index (NDLI), to monitor drought conditions. The results show that the NDLI has a high correlation with NDVI (r = 0.96), precipitation (r = 0.81), soil moisture (r = 0.73), and LST (r = −0.67). NDLI successfully captures the historical droughts and shows a notable correlation with the climatic variables. The analysis shows that using the radiances of green, red, and short wave infrared (SWIR), a simplified crop monitoring model with satisfactory accuracy and easiness can be developed.

scholarly journals Assessing the Evolution of Soil Moisture and Vegetation Conditions during a Flash Drought–Flash Recovery Sequence over the South-Central United States

2019 ◽  
Vol 20 (3) ◽  
pp. 549-562 ◽  
Author(s):  
Jason A. Otkin ◽  
Yafang Zhong ◽  
Eric D. Hunt ◽  
Jeff Basara ◽  
Mark Svoboda ◽  
...  
Keyword(s):  
United States ◽  
Soil Moisture ◽  
Land Surface ◽  
Heavy Rainfall ◽  
Recovery Period ◽  
The South ◽  
Recovery Sequence ◽  
Near Surface ◽  
South Central ◽  
Central United States

Abstract This study examines the evolution of soil moisture, evapotranspiration, vegetation, and atmospheric conditions during an unusual flash drought–flash recovery sequence that occurred across the south-central United States during 2015. This event was characterized by a period of rapid drought intensification (flash drought) during late summer that was terminated by heavy rainfall at the end of October that eliminated the extreme drought conditions over a 2-week period (flash recovery). A detailed analysis was performed using time series of environmental variables derived from meteorological, remote sensing, and land surface modeling datasets. Though the analysis revealed a similar progression of cascading effects in each region, characteristics of the flash drought such as its onset time, rate of intensification, and vegetation impacts differed between regions due to variations in the antecedent conditions and the atmospheric anomalies during its growth. Overall, flash drought signals initially appeared in the near-surface soil moisture, followed closely by reductions in evapotranspiration. Total column soil moisture deficits took longer to develop, especially in the western part of the region where heavy rainfall during the spring and early summer led to large moisture surpluses. Large differences were noted in how land surface models in the North American Land Data Assimilation System depicted soil moisture evolution during the flash drought; however, the models were more similar in their assessment of conditions during the flash recovery period. This study illustrates the need to use multiple datasets to track the evolution and impacts of rapidly evolving flash drought and flash recovery events.

scholarly journals Numerical Simulations of Effects of Soil Moisture and Modification by Mountains over New Zealand in Summer

2011 ◽  
Vol 139 (2) ◽  
pp. 494-510 ◽  
Author(s):  
Yang Yang ◽  
Michael Uddstrom ◽  
Mike Revell ◽  
Phil Andrews ◽  
Hilary Oliver ◽  
...  
Keyword(s):  
Soil Moisture ◽  
New Zealand ◽  
Moisture Content ◽  
Air Temperature ◽  
Land Surface ◽  
Soil Moisture Content ◽  
Surface Air Temperature ◽  
Shortwave Radiation ◽  
Lower Sensitivity ◽  
The South

Abstract Historically most soil moisture–land surface impact studies have focused on continents because of the important forecasting and climate implications involved. For a relatively small isolated mountainous landmass in the ocean such as New Zealand, these impacts have received less attention. This paper addresses some of these issues for New Zealand through numerical experiments with a regional configuration of the Met Office Unified Model atmospheric model. Two pairs of idealized simulations with only contrasting dry or wet initial soil moisture over a 6-day period in January 2004 were conducted, with one pair using realistic terrain and the other pair flat terrain. For the mean of the 6 days, the differences in the simulated surface air temperature between the dry and moist cases were 3–5 K on the leeside slopes and 1–2 K on the windward slopes and the central leeside coastal region of the South Island in the afternoon. This quite nonuniform response in surface air temperature to a uniformly distributed soil moisture content and soil type is mainly attributed to modification of the effects of soil moisture by mountains through two different processes: 1) spatial variation in cloud coverage across the mountains ranges leading to more shortwave radiation at ground surface on the leeside slope than the windward slope, and 2) the presence of a dynamically and thermally induced onshore flow on the leeside coast bringing in air with a lower sensitivity to soil moisture. The response of local winds to soil moisture content is through direct or indirect effects. The direct effect is due to the thermal contrast between land and sea/land shown for the leeside solenoidal circulations, and the indirect effect is through the weakening of the upstream blocking of the South Island for dryer soils shown by the weakening and onshore shift of the upstream deceleration and forced ascent of incoming airflow.

scholarly journals Soil Moisture Variability in India: Relationship of Land Surface–Atmosphere Fields Using Maximum Covariance Analysis

2019 ◽  
Vol 11 (3) ◽  
pp. 335 ◽  
Author(s):  
Kishore Pangaluru ◽  
Isabella Velicogna ◽  
Geruo A ◽  
Yara Mohajerani ◽  
Enrico Ciracì ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Air Temperature ◽  
Land Surface ◽  
Water Storage ◽  
Surface Air Temperature ◽  
Covariance Analysis ◽  
Positive Trend ◽  
Total Water ◽  
Maximum Covariance Analysis ◽  
Surface Atmosphere

This study investigates the spatial and temporal variability of the soil moisture in India using Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) gridded datasets from June 2002 to April 2017. Significant relationships between soil moisture and different land surface–atmosphere fields (Precipitation, surface air temperature, total cloud cover, and total water storage) were studied, using maximum covariance analysis (MCA) to extract dominant interactions that maximize the covariance between two fields. The first leading mode of MCA explained 56%, 87%, 81%, and 79% of the squared covariance function (SCF) between soil moisture with precipitation (PR), surface air temperature (TEM), total cloud count (TCC), and total water storage (TWS), respectively, with correlation coefficients of 0.65, −0.72, 0.71, and 0.62. Furthermore, the covariance analysis of total water storage showed contrasting patterns with soil moisture, especially over northwest, northeast, and west coast regions. In addition, the spatial distribution of seasonal and annual trends of soil moisture in India was estimated using a robust regression technique for the very first time. For most regions in India, significant positive trends were noticed in all seasons. Meanwhile, a small negative trend was observed over southern India. The monthly mean value of AMSR soil moisture trend revealed a significant positive trend, at about 0.0158 cm3/cm3 per decade during the period ranging from 2002 to 2017.

scholarly journals A bare ground evaporation revision in the ECMWF land-surface scheme: evaluation of its impact using ground soil moisture and satellite microwave data

2012 ◽  
Vol 16 (10) ◽  
pp. 3607-3620 ◽  
Author(s):  
C. Albergel ◽  
G. Balsamo ◽  
P. de Rosnay ◽  
J. Muñoz-Sabater ◽  
S. Boussetta
Keyword(s):  
Soil Moisture ◽  
Brightness Temperature ◽  
Land Surface ◽  
The United States ◽  
Microwave Emission ◽  
Bare Ground ◽  
New Formulation ◽  
The Impact ◽  
Surface Fields

Abstract. In situ soil moisture data from 122 stations across the United States are used to evaluate the impact of a new bare ground evaporation formulation at ECMWF. In November 2010, the bare ground evaporation used in ECMWF's operational Integrated Forecasting System (IFS) was enhanced by adopting a lower stress threshold than for the vegetation, allowing a higher evaporation. It results in more realistic soil moisture values when compared to in situ data, particularly over dry areas. Use was made of the operational IFS and offline experiments for the evaluation. The latter are based on a fixed version of the IFS and make it possible to assess the impact of a single modification, while the operational analysis is based on a continuous effort to improve the analysis and modelling systems, resulting in frequent updates (a few times a year). Considering the field sites with a fraction of bare ground greater than 0.2, the root mean square difference (RMSD) of soil moisture is shown to decrease from 0.118 m3 m−3 to 0.087 m3 m−3 when using the new formulation in offline experiments, and from 0.110 m3 m−3 to 0.088 m3 m−3 in operations. It also improves correlations. Additionally, the impact of the new formulation on the terrestrial microwave emission at a global scale is investigated. Realistic and dynamically consistent fields of brightness temperature as a function of the land surface conditions are required for the assimilation of the SMOS data. Brightness temperature simulated from surface fields from two offline experiments with the Community Microwave Emission Modelling (CMEM) platform present monthly mean differences up to 7 K. Offline experiments with the new formulation present drier soil moisture, hence simulated brightness temperature with its surface fields are larger. They are also closer to SMOS remotely sensed brightness temperature.

Investigating the Relationship between the Evaporative Stress Index and Land Surface Conditions in the Contiguous United States

2020 ◽  
Vol 21 (7) ◽  
pp. 1469-1484
Author(s):  
Yafang Zhong ◽  
Jason A. Otkin ◽  
Martha C. Anderson ◽  
Christopher Hain
Keyword(s):  
United States ◽  
Soil Moisture ◽  
Great Plains ◽  
Land Surface ◽  
The United States ◽  
Stress Index ◽  
Northern Great Plains ◽  
Reference Network ◽  
North Central ◽  
Central United States

AbstractDespite the key importance of soil moisture–evapotranspiration (ET) coupling in the climate system, limited availability of soil moisture and ET observations poses a major impediment for investigation of this coupling regarding spatiotemporal characteristics and potential modifications under climate change. To better understand and quantify soil moisture–ET coupling and relevant processes, this study takes advantage of in situ soil moisture observations from the U.S. Climate Reference Network (USCRN) for the time period of 2010–17 and a satellite-derived version of the evapotranspiration stress index (ESI), which represents anomalies in a normalized ratio of actual to reference ET. The analyses reveal strong seasonality and regional characteristics of the ESI–land surface interactions across the United States, with the strongest control of soil moisture on the ESI found in the southern Great Plains during spring, and in the north-central United States, the northern Great Plains, and the Pacific Northwest during summer. In drier climate regions such as the northern Great Plains and north-central United States, soil moisture control on the ESI is confined to surface soil layers, with subsurface soil moisture passively responding to changes in the ESI. The soil moisture–ESI interaction is more uniform between surface and subsurface soils in wetter regions with higher vegetation cover. These results provide a benchmark for simulation of soil moisture–ET coupling and are useful for projection of associated climate processes in the future.

scholarly journals LDAS-Monde Sequential Assimilation of Satellite Derived Observations Applied to the CONtiguous US: An ERA-5 Driven Reanalysis of the Land Surface Variables

2018 ◽  
Author(s):  
Clément Albergel ◽  
Simon Munier ◽  
Aymeric Bocher ◽  
Bertrand Bonan ◽  
Yongjun Zheng ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Initial Conditions ◽  
The United States ◽  
Agricultural Drought ◽  
Reference Network ◽  
Surface Model ◽  
Area Index ◽  
Medium Range Weather Forecast ◽  
The Impact

LDAS-Monde, an offline land data assimilation system with global capacity, is applied over the CONtiguous US (CONUS) domain to enhance monitoring accuracy for water and energy states and fluxes. LDAS-Monde ingests satellite-derived Surface Soil Moisture (SSM) and Leaf Area Index (LAI) estimates to constrain the Interactions between Soil, Biosphere, and Atmosphere (ISBA) Land Surface Model (LSM) coupled with the CNRM (Centre National de Recherches Météorologiques) version of the Total Runoff Integrating Pathways (CTRIP) continental hydrological system (ISBA-CTRIP). LDAS-Monde is forced by the ERA-5 atmospheric reanalysis from the European Center For Medium Range Weather Forecast (ECMWF) from 2010 to 2016 leading to a 7-yr, quarter degree spatial resolution offline reanalysis of Land Surface Variables (LSVs) over CONUS. The impact of assimilating LAI and SSM into LDAS-Monde is assessed over North America, by comparison to satellite-driven model estimates of land evapotranspiration from the Global Land Evaporation Amsterdam Model (GLEAM) project, and upscaled ground-based observations of gross primary productivity from the FLUXCOM project. Also, taking advantage of the relatively dense data networks over CONUS, we also evaluate the impact of the assimilation against in-situ measurements of soil moisture from the USCRN network (US Climate Reference Network) are used in the evaluation, together with river discharges from the United States Geophysical Survey (USGS) and the Global Runoff Data Centre (GRDC). Those data sets highlight the added value of assimilating satellite derived observations compared to an open-loop simulation (i.e. no assimilation). It is shown that LDAS-Monde has the ability not only to monitor land surface variables but also to forecast them, by providing improved initial conditions which impacts persist through time. LDAS-Monde reanalysis has a potential to be used to monitor extreme events like agricultural drought, also. Finally, limitations related to LDAS-Monde and current satellite-derived observations are exposed as well as several insights on how to use alternative datasets to analyze soil moisture and vegetation state.

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