scholarly journals Opportunity for GNSS Reflectometry in Sensing the Regional Climate and Soil Moisture Instabilities in Myanmar

Climate ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 175
Author(s):  
Aung Lwin ◽  
Dongkai Yang ◽  
Xuebao Hong ◽  
Bo Zhang ◽  
Baoyin Zhang ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Surface Temperature ◽  
Monsoon Season ◽  
Regional Climate ◽  
Storm Surges ◽  
Weather Conditions ◽  
Regional Study ◽  
Gnss Reflectometry ◽  
Surface Reflectivity ◽  
Climate Crisis

The climate crisis is happening globally, and the consequent process has revealed soil evolution and meteorological interactions. The GNSS reflectometry (GNSS-R) technique recently encompassed sea surface monitoring, land changes, and snow sensing in addition to position, navigation, and timing. After the launch of NASA’s eight CYGNSS satellites, spaceborne soil moisture retrieval has become more opportune in a global and regional investigation. The research carried out by the CYGNSS DDM SNR with SMAP data to correlate diurnal mean soil moisture sensing was analyzed in the regional study of Myanmar, which is prone to climatic and weather conditions. The results showed that spaceborne GNSS-R soil moisture sensitivity was very useful during seasonal changes in regional observation. The DDM SNR surface reflectivity was strongly correlated with soil moisture according to surface temperature variations prepared from SMAP passive reflectometry. Sentinel SAR-1 data included the validation and verification of flood-prone areas affected by tropical storm surges or weather depressions in the monsoon season. The availability of surface reflectivity primarily relied on the surface roughness, surface temperature, and vegetation opacity for soil moisture retrieval.

Spaceborne GNSS Reflectometry for soil moisture using data from CYGNSS mission: results in the Contiguous United States

2021 ◽  
Author(s):  
Paulo de Tarso Setti Junior ◽  
Tonie van Dam
Keyword(s):  
United States ◽  
Soil Moisture ◽  
Optical Depth ◽  
Roughness Coefficient ◽  
Navigation Satellite ◽  
Gnss Reflectometry ◽  
Surface Reflectivity ◽  
Global Navigation ◽  
Global Navigation Satellite

<p>Soil moisture is an essential climate variable, influencing geophysical and hydrological processes such as vegetation and agriculture, land-atmosphere circulation, and drought development. It is possible to remotely sense soil moisture based on the dielectric constant of soil at microwave frequencies. Low earth orbit (LEO) satellites are capable of receiving Global Navigation Satellite Systems (GNSS) signals reflected off the surface of the Earth to infer properties of the reflecting surface itself, in a technique known as GNSS-Reflectometry (GNSS-R). However, converting surface reflectivity derived from GNSS-R into soil moisture is not straightforward. Reflectivity is influenced by other factors such as the vegetation optical depth and the soil roughness around the specular reflection. The Cyclone Global Navigation Satellite System (CYGNSS) is a mission from the National Aeronautics and Space Administration (NASA) consisting of eight small GNSS-R satellites with the primary objective of measuring wind speed in hurricanes and tropical cyclones. The satellites were launched in December 2016 in a 35° inclination orbit, and the measurements are made of reflected Global Positioning System (GPS) L1 (1.575 GHz) navigation signals. Reflections over land can be used to estimate soil moisture in the upper 5 cm of soil surface if they are correctly treated and modelled. In this work, we use three years of observations from CYGNSS mission (March 2017 - March 2020) to compute surface reflectivity over land assuming coherent reflections. Using linear regression models and ancillary information from Soil Moisture Active Passive (SMAP) mission (soil moisture, vegetation optical depth, and roughness coefficient), these reflectivity observations are then used to estimate soil moisture. Retrievals are compared with observations from 44 in-situ soil moisture stations from the International Soil Moisture Network (ISMN) in the Contiguous United States (CONUS), presenting in most of the cases a good agreement. Results are also correlated with vegetation optical depth, surface roughness, and topographic relief around the in-situ stations. In addition, some challenges regarding soil moisture estimation using spaceborne GNSS-R data are presented and discussed.</p>

scholarly journals Soil Moisture–Precipitation Feedback Processes in the Indian Summer Monsoon Season

2012 ◽  
Vol 13 (5) ◽  
pp. 1461-1474 ◽  
Author(s):  
Shakeel Asharaf ◽  
Andreas Dobler ◽  
Bodo Ahrens
Keyword(s):  
Soil Moisture ◽  
Summer Monsoon ◽  
Indian Summer Monsoon ◽  
Land Surface ◽  
Climate Model ◽  
Monsoon Season ◽  
Regional Climate ◽  
Precipitable Water ◽  
Small Scale ◽  
Summer Monsoon Season

Abstract Soil moisture can influence precipitation through a feedback loop with land surface evapotranspiration. A series of numerical simulations, including soil moisture sensitivity experiments, have been performed for the Indian summer monsoon season (ISM). The simulations were carried out with the nonhydrostatic regional climate model Consortium for Small-Scale Modeling (COSMO) in climate mode (COSMO-CLM), driven by lateral boundary conditions derived from the ECMWF Interim reanalysis (ERA-Interim). Positive as well as negative feedback processes through local and remote effects are shown to be important. The regional moisture budget studies have exposed that changes in precipitable water and changes in precipitation efficiency vary in importance, in time, and in space in the simulations for India. Overall, the results show that the premonsoonal soil moisture has a significant influence on the monsoonal precipitation, and thus confirmed that modeling of soil moisture is essential for reliable simulation and forecasting of the ISM.

scholarly journals The Sensitivity of Land–Atmosphere Coupling to Modern Agriculture in the Northern Midlatitudes

2018 ◽  
Vol 32 (2) ◽  
pp. 465-484
Author(s):  
Sonali Shukla McDermid ◽  
Carlo Montes ◽  
Benjamin I. Cook ◽  
Michael J. Puma ◽  
Nancy Y. Kiang ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Cover ◽  
Surface Temperature ◽  
Regional Climate ◽  
Growing Season ◽  
Climate Impacts ◽  
Surface Radiation ◽  
Climate Conditions ◽  
Simulated Precipitation ◽  
Background Climate

AbstractModern agricultural land cover and management are important as regional climate forcings. Previous work has shown that land cover change can significantly impact key climate variables, including turbulent fluxes, precipitation, and surface temperature. However, fewer studies have investigated how intensive crop management can impact background climate conditions, such as the strength of land–atmosphere coupling and evaporative regime. We conduct sensitivity experiments using a state-of-the-art climate model with modified vegetation characteristics to represent modern crop cover and management, using observed crop-specific leaf area indexes and calendars. We quantify changes in land–atmosphere interactions and climate over intensively cultivated regions situated at transitions between moisture- and energy-limited conditions. Results show that modern intensive agriculture has significant and geographically varying impacts on regional evaporative regimes and background climate conditions. Over the northern Great Plains, modern crop intensity increases the model simulated precipitation and soil moisture, weakening hydrologic coupling by increasing surface water availability and reducing moisture limits on evapotranspiration. In the U.S. Midwest, higher growing season evapotranspiration, coupled with winter and spring rainfall declines, reduces regional soil moisture, while crop albedo changes also reduce net surface radiation. This results overall in reduced dependency of regional surface temperature on latent heat fluxes. In central Asia, a combination of reduced net surface energy and enhanced pre–growing season precipitation amplify the energy-limited evaporative regime. These results highlight the need for improved representations of agriculture in global climate models to better account for regional climate impacts and interactions with other anthropogenic forcings.

Buster Keaton’s Climate Change

2018 ◽  
Author(s):  
Jennifer Fay
Keyword(s):  
Climate Change ◽  
World War I ◽  
Storm Surges ◽  
Weather Conditions ◽  
Detailed Knowledge ◽  
World War ◽  
Precise Control ◽  
Naturally Occurring ◽  
Peter Sloterdijk ◽  
Slapstick Comedy

Much of Buster Keaton’s slapstick comedy revolves around his elaborate outdoor sets and the crafty weather design that destroys them. In contrast to D. W. Griffith, who insisted on filming in naturally occurring weather, and the Hollywood norm of fabricating weather in the controlled space of the studio, Keaton opted to simulate weather on location. His elaborately choreographed gags with their storm surges and collapsing buildings required precise control of manufactured rain and wind, along with detailed knowledge of the weather conditions and climatological norms on site. Steamboat Bill, Jr. (1928) is one of many examples of Keaton’s weather design in which characters find themselves victims of elements that are clearly produced by the off-screen director. Keaton’s weather design finds parallels in World War I strategies of creating microclimates of death (using poison gas) as theorized by Peter Sloterdijk.

scholarly journals Anthropogenic warming and intraseasonal summer monsoon variability amplify the risk of future flash droughts in India

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Vimal Mishra ◽  
Saran Aadhar ◽  
Shanti Shwarup Mahto
Keyword(s):  
Soil Moisture ◽  
Summer Monsoon ◽  
Crop Production ◽  
Monsoon Rainfall ◽  
Root Zone ◽  
Monsoon Season ◽  
Intraseasonal Variability ◽  
Positive Temperature ◽  
Groundwater Abstraction ◽  
Increased Risk

AbstractFlash droughts cause rapid depletion in root-zone soil moisture and severely affect crop health and irrigation water demands. However, their occurrence and impacts in the current and future climate in India remain unknown. Here we use observations and model simulations from the large ensemble of Community Earth System Model to quantify the risk of flash droughts in India. Root-zone soil moisture simulations conducted using Variable Infiltration Capacity model show that flash droughts predominantly occur during the summer monsoon season (June–September) and driven by the intraseasonal variability of monsoon rainfall. Positive temperature anomalies during the monsoon break rapidly deplete soil moisture, which is further exacerbated by the land-atmospheric feedback. The worst flash drought in the observed (1951–2016) climate occurred in 1979, affecting more than 40% of the country. The frequency of concurrent hot and dry extremes is projected to rise by about five-fold, causing approximately seven-fold increase in flash droughts like 1979 by the end of the 21st century. The increased risk of flash droughts in the future is attributed to intraseasonal variability of the summer monsoon rainfall and anthropogenic warming, which can have deleterious implications for crop production, irrigation demands, and groundwater abstraction in India.

scholarly journals Representation of Soil Moisture Feedbacks during Drought in NASA Unified WRF (NU-WRF)

2013 ◽  
Vol 14 (1) ◽  
pp. 360-367 ◽  
Author(s):  
Benjamin F. Zaitchik ◽  
Joseph A. Santanello ◽  
Sujay V. Kumar ◽  
Christa D. Peters-Lidard
Keyword(s):  
Soil Moisture ◽  
Great Plains ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Summer Drought ◽  
Dynamic Surface ◽  
Seasonal Drought ◽  
Weather Forecasts ◽  
Local Soil

Abstract Positive soil moisture–precipitation feedbacks can intensify heat and prolong drought under conditions of precipitation deficit. Adequate representation of these processes in regional climate models is, therefore, important for extended weather forecasts, seasonal drought analysis, and downscaled climate change projections. This paper presents the first application of the NASA Unified Weather Research and Forecasting Model (NU-WRF) to simulation of seasonal drought. Simulations of the 2006 southern Great Plains drought performed with and without soil moisture memory indicate that local soil moisture feedbacks had the potential to concentrate precipitation in wet areas relative to dry areas in summer drought months. Introduction of a simple dynamic surface albedo scheme that models albedo as a function of soil moisture intensified the simulated feedback pattern at local scale—dry, brighter areas received even less precipitation while wet, whereas darker areas received more—but did not significantly change the total amount of precipitation simulated across the drought-affected region. This soil-moisture-mediated albedo land–atmosphere coupling pathway is structurally excluded from standard versions of WRF.

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