The Impacts of Indirect Soil Moisture Assimilation and Direct Surface Temperature and Humidity Assimilation on a Mesoscale Model Simulation of an Indian Monsoon Depression

Vinodkumar,; Chandrasekar, A.; Alapaty, Kiran; Niyogi, Dev

doi:10.1175/2007jamc1599.1

Cited by 31 publications

(17 citation statements)

References 36 publications

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“…This rather complicated dynamical and hydrological processes of the MD www.intechopen.com provide some challenges to the regional and global climate models. For example, to properly simulate extreme rainfall under global warming, these need to be well simulated by regional or global climate models (e.g., Ratnan and Cox 2006, Vinodkumar et al 2008). …”

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confidence: 99%

Indian Monsoon Depression: Climatology and Variability

Yoon¹,

Huang²

2012

Modern Climatology

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confidence: 99%

Indian Monsoon Depression: Climatology and Variability

Yoon¹,

Huang²

2012

Modern Climatology

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“…They found also that whenever there is a local forcing, the analysis-nudging alone, without the observation-nudging, may be harmful because it could limit the model's natural ability to produce finer-scale features. Something different has been found by Vinodkumar et al (2008) and Vinodkumar et al (2009) who studied the Indian monsoon region. They reported a general improvement of the meteorological parameters with surface and satellite data assimilation even in these strongly largescale-induced conditions.…”

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confidence: 62%

“…-as other authors have indicated (Vinodkumar et al, 2008 for example), it is probably worth trying the assimilation of surface variables together with vertical profiles of temperature and relative humidity, or maybe also using the latent heat nudging approach (see for instance the work of Leuenberger and Rossa, 2007) or assimilation of satellite data and fluxes or GPS data. Those modifications could be helpful to adjust the entire PBL in order to correct the precipitation field.…”

Section: Conclusion and Future Perspectivesmentioning

confidence: 99%

Screen-level non-GTS data assimilation in a limited-area mesoscale model

Milelli¹,

Turco

Oberto³

2010

Nat. Hazards Earth Syst. Sci.

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Abstract. The forecast in areas of very complex topography, as for instance the Alpine region, is still a challenge even for the new generation of numerical weather prediction models which aim at reaching the km-scale. The problem is enhanced by a general lack of standard observations, which is even more evident over the southern side of the Alps. For this reason, it would be useful to increase the performance of the mathematical models by locally assimilating non-conventional data. Since in ARPA Piemonte there is the availability of a great number of non-GTS stations, it has been decided to assimilate the 2 m temperature, coming from this dataset, in the very-high resolution version of the COSMO model, which has a horizontal resolution of about 3 km, more similar to the average resolution of the thermometers. Four different weather situations have been considered, ranging from spring to winter, from cloudy to clear sky. The aim of the work is to investigate the effects of the assimilation of non-GTS data in order to create an operational very high-resolution analysis, but also to test the option of running in the future a very short-range forecast starting from these analyses (RUC or Rapid Update Cycle). The results, in terms of Root Mean Square Error, Mean Error and diurnal cycle of some surface variables such as 2 m temperature, 2 m relative humidity and 10 m wind intensity show a positive impact during the assimilation cycle which tends to dissipate a few hours after the end of it. Moreover, the 2 m temperature assimilation has a slightly positive or neutral impact on the vertical profiles of temperature, eventhough some calibration is needed for the precipitation field which is too much perturbed during the assimilation cycle, while it is unaffected in the forecast period. So the stability of the planetary boundary layer, on the one hand, has not been particu- Milelli (m.milelli@arpa.piemonte.it) larly improved by the new-data assimilation, but, on the other hand, it has not been destroyed. It has to be pointed out that a correct description of the planetary boundary layer, even only the lowest part of it, could be helpful to the forecasters and, in general, to the users, in order to deal with meteorological hazards such as snow (in particular snow/rain limit definition), or fog (description of temperature inversions).

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“…Ramamurthy and Carr 1987;Krishnamurti et al 1991), the impact of assimilating land-surface information is relatively poorly studied (e.g. Vinodkumar et al 2008).…”

Section: Introductionmentioning

confidence: 99%

Assessment of Data Assimilation Approaches for the Simulation of a Monsoon Depression Over the Indian Monsoon Region

Vinodkumar

Chandrasekar

Alapaty

et al. 2009

Boundary-Layer Meteorol

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A low pressure system that formed on 21 September 2006 over eastern India/Bay of Bengal intensified into a monsoon depression resulting in copious rainfall over north-eastern and central parts of India. Four numerical experiments are performed to examine the performance of assimilation schemes in simulating this monsoon depression using the Fifth Generation Mesoscale Model (MM5). Forecasts from a base simulation (with no data assimilation), a four-dimensional data assimilation (FDDA) system, a simple surface data assimilation (SDA) system coupled with FDDA, and a flux-adjusting SDA system (FASDAS) coupled with FDDA are compared with each other and with observations. The model is initialized with Global Forecast System (GFS) forecast fields starting from 19 September 2006, with assimilation being done for the first 24 hours using conventional observations, sounding and surface data of temperature and moisture from Advanced TIROS Operational Vertical Sounder satellite and surface wind data over the ocean from QuikSCAT. Forecasts are then made from these assimilated states. In general, results indicate that the FASDAS forecast provides more realistic prognostic fields as compared to the other three forecasts. When compared with other forecasts, results indicate that the FASDAS forecast yielded lower root-mean-square (r.m.s.) errors for the pressure field and improved simulations of surface/near-surface temperature, moisture, sensible and latent heat fluxes, and potential 123 344 Vinodkumar et al.vorticity. Heat and moisture budget analyses to assess the simulation of convection revealed that the two forecasts with the surface data assimilation (SDA and FASDAS) are superior to the base and FDDA forecasts. An important conclusion is that, even though monsoon depressions are large synoptic systems, mesoscale features including rainfall are affected by surface processes. Enhanced representation of land-surface processes provides a significant improvement in the model performance even under active monsoon conditions where the synoptic forcings are expected to be dominant.

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The Impacts of Indirect Soil Moisture Assimilation and Direct Surface Temperature and Humidity Assimilation on a Mesoscale Model Simulation of an Indian Monsoon Depression

Cited by 31 publications

References 36 publications

Indian Monsoon Depression: Climatology and Variability

Indian Monsoon Depression: Climatology and Variability

Screen-level non-GTS data assimilation in a limited-area mesoscale model

Assessment of Data Assimilation Approaches for the Simulation of a Monsoon Depression Over the Indian Monsoon Region

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