scholarly journals Impact of Soil Moisture Initialization in the Simulation of Indian Summer Monsoon Using RegCM4

Atmosphere ◽  
2021 ◽  
Vol 12 (9) ◽  
pp. 1148
Author(s):  
Suman Maity ◽  
Sridhara Nayak ◽  
Kuvar Satya Singh ◽  
Hara Prasad Nayak ◽  
Soma Dutta
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
Surface Temperature ◽  
Summer Monsoon ◽  
Indian Summer Monsoon ◽  
Careful Examination ◽  
Soil Moisture Data ◽  
Statistical Measures ◽  
The Impact ◽  
Soil Moisture Initialization

Soil moisture is one of the key components of land surface processes and a potential source of atmospheric predictability that has received little attention in regional scale studies. In this study, an attempt was made to investigate the impact of soil moisture on Indian summer monsoon simulation using a regional model. We conducted seasonal simulations using a regional climate model (RegCM4) for two different years, viz., 2002 (deficit) and 2011 (normal). The model was forced to initialize with the high-resolution satellite-derived soil moisture data obtained from the Climate Change Initiative (CCI) of the European Space Agency (ESA) by replacing the default static soil moisture. Simulated results were validated against high-resolution surface temperature and rainfall analysis datasets from the India Meteorology Department (IMD). Careful examination revealed significant advancement in the RegCM4 simulation when initialized with soil moisture data from ESA-CCI despite having regional biases. In general, the model exhibited slightly higher soil moisture than observation, RegCM4 with ESA setup showed lower soil moisture than the default one. Model ability was relatively better in capturing surface temperature distribution when initialized with high-resolution soil moisture data. Rainfall biases over India and homogeneous regions were significantly improved with the use of ESA-CCI soil moisture data. Several statistical measures such as temporal correlation, standard deviation, equitable threat score (ETS), etc. were also employed for the assessment. ETS values were found to be better in 2011 and higher in the simulation with the ESA setup. However, RegCM4 was still unable to enhance its ability in simulating temporal variation of rainfall adequately. Although initializing with the soil moisture data from the satellite performed relatively better in a normal monsoon year (2011) but had limitations in simulating different epochs of monsoon in an extreme year (2002). Thus, the study concluded that the simulation of the Indian summer monsoon was improved by using RegCM4 initialized with high-resolution satellite soil moisture data although having limitations in predicting temporal variability. The study suggests that soil moisture initialization has a critical impact on the accurate prediction of atmospheric circulation processes and convective rainfall activity.

UAS Based Soil Moisture Downscaling Using Random Forest Regression Model

2021 ◽  
Author(s):  
Ruodan Zhuang ◽  
Salvatore Manfreda ◽  
Yijian Zeng ◽  
Nunzio Romano ◽  
Eyal Ben Dor ◽  
...  
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
Random Forest ◽  
Surface Temperature ◽  
Regression Model ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Surface Features ◽  
Coarse Resolution ◽  
Soil Moisture Data

<p>Soil moisture (SM) is an essential element in the hydrological cycle influencing land-atmosphere interactions and rainfall-runoff processes. High-resolution mapping of SM at field scale is vital for understanding spatial and temporal behavior of water availability in agriculture. Unmanned Arial Systems (UAS) offer an extraordinary opportunity to bridge the existing gap between point-scale field observations and satellite remote sensing providing high spatial details at relatively low costs. Moreover, this data can help the construction of downscaling models to generate high-resolution SM maps. For instance, random Forest (RF) regression model can link the land surface features and SM to identify the importance level of each predictor.</p><p>The RF regression model has been tested using a combination of satellite imageries, UAS data and point measurements collected on the experimental area Monteforte Cilento site (MFC2) in the Alento river basin (Campania, Italy) which is an 8 hectares cropland area (covered by walnuts, cherry, and olive trees). This area has been selected given the number of long-term studies on the vadose zone that have been conducted across a range of spatial scales.</p><p>The coarse resolution data cover from Jan 2015 to Dec 2019 and include SENTINEL-1 CSAR 1km SM product, 1km Land surface temperature and NDVI products from MODIS and 30m thermal band (brightness temperature), red and green band data (atmospherically corrected surface reflectance) from LANDSAT-8, and SRTM DEM from NASA. High-resolution land-surface features data from UAS-mounted optical, thermal, multispectral, and hyperspectral sensors were used to generate high-resolution SM and related soil attributes.</p><p>It is to note that the available satellite-based soil moisture data has a coarse resolution of 1km while the UAS-based land surface features of the extremely high resolution of 16cm. We deployed a two-step downscaling approach to address the smooth effect of spatial averaging of soil moisture, which depends on different elements at small and large scale. Specifically, different combinations of predictors were adopted for different scales of gridded soil moisture data. For example, in the downscaling procedure from 1km resolution to 30m resolution, precipitation, land-surface temperature (LST), vegetation indices (VIs), and elevation were used while LST, VIs, slope, and topographic index were selected for the downscaling from 30m to 16cm resolution. Indeed, features controlling the spatial distributions of soil moisture at different scale reflect the characteristics of the physical process: i) the surface elevation and rainfall patterns control the first downscaling model; ii) the topographic convergence and local slope become more relevant to reach a more detailed resolution. In conclusion, the study highlighted that RF regression model is able to interpret fairly well the spatial patterns of soil moisture at the scale of 30m starting from a resolution of 1km, while it is highlighted that the second downscaling step (up to few centimeters) is much more complex and requires further studies.</p><p>This research is a part of EU COST-Action “HARMONIOUS: Harmonization of UAS techniques for agricultural and natural ecosystems monitoring”.</p><p><strong>Keywords:</strong> soil moisture, downscaling, Unmanned Aerial Systems, random forest, HARMONIOUS</p>

scholarly journals Seasonal predictability of the Indian Summer Monsoon : What role do land surface conditions play?

MAUSAM ◽  
2021 ◽  
Vol 52 (1) ◽  
pp. 175-190
Author(s):  
B. D. BECKER ◽  
J. M. SLINGO ◽  
L. FERRANTI ◽  
F. MOLTENI
Keyword(s):  
Surface Temperature ◽  
Summer Monsoon ◽  
Indian Summer Monsoon ◽  
Land Surface ◽  
Southern Oscillation ◽  
Monsoon Circulation ◽  
Surface Conditions ◽  
Monsoon Variability ◽  
Western Eurasia ◽  
The Impact

Anomalous springtime snow amounts over Eurasia may provide long term memory to the climate system by affecting the land surface energy and moisture budgets. In turn the anomalous land surface conditions introduced by snow anomalies may influence monsoon variability. In this paper, results from a programme of seasonal forecast ensembles are used to address, specifically, the influence of western Eurasian land surface conditions on the variability and hence predictability of the Indian summer monsoon. The factors that are important for establishing spring time land surface conditions over western Eurasia, particularly snow amounts are also investigated.   The results have shown that high snow amounts over western Eurasia are linked to La Nina, suggesting that the El-Nino/Southern Oscillation (ENSO) has an influence on the wintertime climate of Eurasia. The signature of these snow depth anomalies is carried through to the summer in terms of changes in soil wetness and surface temperature. An ensemble of summer integrations with climatological sea surface temperature (SST) has been used to investigate the impact of these anomalous land surface conditions on monsoon variability. The results have shown that the monsoon circulation is substantially weakened in association with above normal snow amounts over western Eurasia, whilst All India Rainfall is slightly increased. Results from a parallel ensemble with observed SSTs show an opposite response in All India Rainfall, suggesting that the forcing by SST anomalies is potentially dominating the monsoon's inter-annual variability.   The results have demonstrated that land surface conditions can have a significant impact on the large scale monsoon circulation and to a lesser extent on Indian Summer Monsoon rainfall, although the mechanisms involved have yet to be identified. It is suggested that interactions between the mid-latitude circulation and the monsoon may hold the key to understanding the link between Eurasian land surface conditions and monsoon variability. If that is the case then predictability of this relationship is likely to be limited, due to the high level of internal variability of the mid-latitude circulation.

scholarly journals Impact of a Narrow Coastal Bay of Bengal Sea Surface Temperature Front on an Indian Summer Monsoon Simulation

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Dhrubajyoti Samanta ◽  
Saji N. Hameed ◽  
Dachao Jin ◽  
Vishnu Thilakan ◽  
Malay Ganai ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Summer Monsoon ◽  
Indian Summer Monsoon ◽  
Bay Of Bengal ◽  
Sea Surface ◽  
Temperature Front ◽  
Coastal Bay

scholarly journals Impact of ASAR soil moisture data on the MM5 precipitation forecast for the Tanaro flood event of April 2009

2011 ◽  
Vol 11 (12) ◽  
pp. 3135-3149 ◽  
Author(s):  
G. Panegrossi ◽  
R. Ferretti ◽  
L. Pulvirenti ◽  
N. Pierdicca
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
Land Surface ◽  
Ground Truth ◽  
Rain Gauge ◽  
Precipitation Forecast ◽  
Flood Event ◽  
Strong Impact ◽  
Moisture Field ◽  
The Impact

Abstract. The representation of land-atmosphere interactions in weather forecast models has a strong impact on the Planetary Boundary Layer (PBL) and, in turn, on the forecast. Soil moisture is one of the key variables in land surface modelling, and an inadequate initial soil moisture field can introduce major biases in the surface heat and moisture fluxes and have a long-lasting effect on the model behaviour. Detecting the variability of soil characteristics at small scales is particularly important in mesoscale models because of the continued increase of their spatial resolution. In this paper, the high resolution soil moisture field derived from ENVISAT/ASAR observations is used to derive the soil moisture initial condition for the MM5 simulation of the Tanaro flood event of April 2009. The ASAR-derived soil moisture field shows significantly drier conditions compared to the ECMWF analysis. The impact of soil moisture on the forecast has been evaluated in terms of predicted precipitation and rain gauge data available for this event have been used as ground truth. The use of the drier, highly resolved soil moisture content (SMC) shows a significant impact on the precipitation forecast, particularly evident during the early phase of the event. The timing of the onset of the precipitation, as well as the intensity of rainfall and the location of rain/no rain areas, are better predicted. The overall accuracy of the forecast using ASAR SMC data is significantly increased during the first 30 h of simulation. The impact of initial SMC on the precipitation has been related to the change in the water vapour field in the PBL prior to the onset of the precipitation, due to surface evaporation. This study represents a first attempt to establish whether high resolution SAR-based SMC data might be useful for operational use, in anticipation of the launch of the Sentinel-1 satellite.

scholarly journals Relative impact of insolation and Warm Pool surface temperature on the East Asia Summer Monsoon during the MIS-13 interglacial

2014 ◽  
Vol 10 (2) ◽  
pp. 1025-1051 ◽  
Author(s):  
Q. Z. Yin ◽  
U. K. Singh ◽  
A. Berger ◽  
Z. T. Guo ◽  
M. Crucifix
Keyword(s):  
Surface Temperature ◽  
Summer Monsoon ◽  
Climate Modeling ◽  
Southern China ◽  
Asian Summer Monsoon ◽  
Summer Precipitation ◽  
Northern China ◽  
Warm Pool ◽  
Pool Surface ◽  
The Impact

Abstract. During Marine Isotope Stage (MIS) 13, an interglacial about 500 000 years ago, the East Asian summer monsoon (EASM) was suggested exceptionally strong by different proxies in China. However, MIS-13 is a weak interglacial in marine oxygen isotope records and has relatively low CO2 and CH4 concentrations compared to other interglacials of the last 800 000 years. In the mean time, the sea surface temperature (SST) reconstructions show that the Western Pacific Warm Pool was relatively warm during MIS-13. Based on climate modeling experiments, this study aims at investigating whether this Warm Pool warming could explain the exceptionally strong EASM occurring during the relatively cool interglacial MIS-13. The individual contributions of insolation and of the Warm Pool SST as well as their synergism are quantified through experiments with the Hadley Centre atmosphere model, HadAM3 and using the factor separation technique. The SST over the Warm Pool region has been increased based on geological reconstructions. Our results show that the pure impact of a strong summer insolation contributes to strengthen significantly the summer precipitation in northern China but only little in southern China. The pure impact of enhanced Warm Pool SST reduces, slightly, the summer precipitation in both northern and southern China. However, the synergism between insolation and enhanced Warm Pool SST contributes to a large increase of summer precipitation in southern China but to a decrease in northern China. Therefore, the ultimate role of enhanced Warm Pool SST reinforces the impact of insolation in southern China but reduces its impact in northern China. We conclude that enhanced SST over the Warm Pool region does help to explain the strong MIS-13 EASM precipitation in southern China as recorded in proxy data, but other explanation is needed for explaining the exceptionally strong EASM in northern China.

scholarly journals Theoretical aspects of the onset of Indian summer monsoon from perturbed orography simulations in a GCM

2006 ◽  
Vol 24 (8) ◽  
pp. 2075-2089 ◽  
Author(s):  
A. Chakraborty ◽  
R. S. Nanjundiah ◽  
J. Srinivasan
Keyword(s):  
Summer Monsoon ◽  
Indian Summer Monsoon ◽  
General Circulation ◽  
Large Scale ◽  
Vertical Motion ◽  
Circulation Model ◽  
Threshold Value ◽  
The Earth ◽  
Static Energy ◽  
The Impact

Abstract. A theory is proposed to determine the onset of the Indian Summer Monsoon (ISM) in an Atmospheric General Circulation Model (AGCM). The onset of ISM is delayed substantially in the absence of global orography. The impact of orography over different parts of the Earth on the onset of ISM has also been investigated using five additional perturbed simulations. The large difference in the date of onset of ISM in these simulations has been explained by a new theory based on the Surface Moist Static Energy (SMSE) and vertical velocity at the mid-troposphere. It is found that onset occurs only after SMSE crosses a threshold value and the large-scale vertical motion in the middle troposphere becomes upward. This study shows that both dynamics and thermodynamics play profound roles in the onset of the monsoon.

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