Search Results
You are looking at 1 - 8 of 8 items for
- Author or Editor: K. Srinivasan x
- Refine by Access: All Content x
Abstract
In many of the short-range numerical weather prediction models, an accurate specification of grid size is essential in mountainous and valley regions to adequately represent terrain forcing without resorting to subgrid-scale parameterization. Since the geomorphology of the earth is quite varied, an improper specification of grid size will neither be adequate nor realistic. In this study, the spectral method is used to determine the required optimum horizontal grid size for numerical simulations enclosing Kashmir Valley and its surroundings.
Abstract
In many of the short-range numerical weather prediction models, an accurate specification of grid size is essential in mountainous and valley regions to adequately represent terrain forcing without resorting to subgrid-scale parameterization. Since the geomorphology of the earth is quite varied, an improper specification of grid size will neither be adequate nor realistic. In this study, the spectral method is used to determine the required optimum horizontal grid size for numerical simulations enclosing Kashmir Valley and its surroundings.
Abstract
The airflow over the Kashmir Valley for a summer day was studied using a numerical mesoscale model. Srinagar observations were used as initial data. The surface orography, soil moisture variations, cloud cover, and vegetation effects were included in the computations. The combined effect of these factors on the development of atmospheric circulations in the valley was obtained quantitatively, and the three-dimensional model simulated results are compared with available observations. The following principal results were obtained. (a) The simulated surface temperature pattern shows a close correlation with the terrain elevations and prevailing atmospheric stabilities, (b) the intensities of katabatic and anabatic winds developed at the slopes are governed by terrain asymmetries and aspect ratio of the slopes, (c) the boundary layer depths developed at different locations in the valley are found to be nonuniform, and (d) the convergence zone formed during nighttime shows an irregular distribution.
Abstract
The airflow over the Kashmir Valley for a summer day was studied using a numerical mesoscale model. Srinagar observations were used as initial data. The surface orography, soil moisture variations, cloud cover, and vegetation effects were included in the computations. The combined effect of these factors on the development of atmospheric circulations in the valley was obtained quantitatively, and the three-dimensional model simulated results are compared with available observations. The following principal results were obtained. (a) The simulated surface temperature pattern shows a close correlation with the terrain elevations and prevailing atmospheric stabilities, (b) the intensities of katabatic and anabatic winds developed at the slopes are governed by terrain asymmetries and aspect ratio of the slopes, (c) the boundary layer depths developed at different locations in the valley are found to be nonuniform, and (d) the convergence zone formed during nighttime shows an irregular distribution.
Abstract
Radiative forcing of aerosols is much more difficult to estimate than that of well-mixed gases due to the large spatial variability of aerosols and the lack of an adequate database on their radiative properties. Estimation of aerosol radiative forcing generally requires knowledge of its chemical composition, which is sparse. Ground-based sky radiance measurements [e.g., aerosol robotic network (AERONET)] can provide key parameters such as the single-scattering albedo, but in shipborne experiments over the ocean it is difficult to make sky radiance measurements and hence these experiments cannot provide parameters such as the single-scattering albedo. However, aerosol spectral optical depth data (cruise based as well as satellite retrieved) are available quite extensively over the ocean. Spectral optical depth measurements have been available since the 1970s, and spectral turbidity measurements (carried out at meteorological departments all over the world) have been available for several decades, while long-term continuous chemical composition information is not available. A new method to differentiate between scattering and absorbing aerosols is proposed here. This can be used to derive simple aerosol models that are optically equivalent and can simulate the observed aerosol optical properties and radiative fluxes, from spectral optical depth measurements. Thus, aerosol single-scattering albedo and, hence, aerosol radiative forcing can be estimated. Note that the proposed method is to estimate clear-sky aerosol radiative forcing (over regions where chemical composition data or sky radiance data are not available) and not to infer its exact chemical composition. Using several independent datasets from field experiments, it is demonstrated that the proposed method can be used to estimate aerosol radiative forcing (from spectral optical depths) with an accuracy of ±2 W m−2.
Abstract
Radiative forcing of aerosols is much more difficult to estimate than that of well-mixed gases due to the large spatial variability of aerosols and the lack of an adequate database on their radiative properties. Estimation of aerosol radiative forcing generally requires knowledge of its chemical composition, which is sparse. Ground-based sky radiance measurements [e.g., aerosol robotic network (AERONET)] can provide key parameters such as the single-scattering albedo, but in shipborne experiments over the ocean it is difficult to make sky radiance measurements and hence these experiments cannot provide parameters such as the single-scattering albedo. However, aerosol spectral optical depth data (cruise based as well as satellite retrieved) are available quite extensively over the ocean. Spectral optical depth measurements have been available since the 1970s, and spectral turbidity measurements (carried out at meteorological departments all over the world) have been available for several decades, while long-term continuous chemical composition information is not available. A new method to differentiate between scattering and absorbing aerosols is proposed here. This can be used to derive simple aerosol models that are optically equivalent and can simulate the observed aerosol optical properties and radiative fluxes, from spectral optical depth measurements. Thus, aerosol single-scattering albedo and, hence, aerosol radiative forcing can be estimated. Note that the proposed method is to estimate clear-sky aerosol radiative forcing (over regions where chemical composition data or sky radiance data are not available) and not to infer its exact chemical composition. Using several independent datasets from field experiments, it is demonstrated that the proposed method can be used to estimate aerosol radiative forcing (from spectral optical depths) with an accuracy of ±2 W m−2.
Abstract
In this study, a one-and-a-half-order Ä“–ε closure scheme is used to study the planetary boundary layer development over a full diurnal cycle using Wangara 33d-day observations as initial conditions. The simulated results are compared with a first-order closure model and higher-order closure model results. A scheme of this kind has the advantage of taking into account the history of turbulence state in terms of a prognostic equation for turbulence kinetic energy and provides a better basis for the representation of clouds. The results of the model simulations compare favorably with other investigators’ results.
Abstract
In this study, a one-and-a-half-order Ä“–ε closure scheme is used to study the planetary boundary layer development over a full diurnal cycle using Wangara 33d-day observations as initial conditions. The simulated results are compared with a first-order closure model and higher-order closure model results. A scheme of this kind has the advantage of taking into account the history of turbulence state in terms of a prognostic equation for turbulence kinetic energy and provides a better basis for the representation of clouds. The results of the model simulations compare favorably with other investigators’ results.
Abstract
Several numerical experiments have been conducted using the NCAR Community Atmosphere Model, version 3 (CAM3) to examine the impact of the time step on rainfall in the intertropical convergence zone (ITCZ) in an aquaplanet. When the model time step was increased from 5 to 60 min the rainfall in the ITCZ decreased substantially. The impact of the time step on the ITCZ rainfall was assessed for a fixed spatial resolution (T63 with L26) for the semi-Lagrangian dynamical core (SLD). The increase in ITCZ rainfall at higher temporal resolution was primarily a result of the increase in large-scale precipitation. This increase in rainfall was caused by the positive feedback between surface evaporation, latent heating, and surface wind speed. Similar results were obtained when the semi-Lagrangian dynamical core was replaced by the Eulerian dynamical core. When the surface evaporation was specified, changes in rainfall were largely insensitive to temporal resolution. The impact of temporal resolution on rainfall was more sensitive to the latitudinal gradient of SST than to the magnitude of SST.
Abstract
Several numerical experiments have been conducted using the NCAR Community Atmosphere Model, version 3 (CAM3) to examine the impact of the time step on rainfall in the intertropical convergence zone (ITCZ) in an aquaplanet. When the model time step was increased from 5 to 60 min the rainfall in the ITCZ decreased substantially. The impact of the time step on the ITCZ rainfall was assessed for a fixed spatial resolution (T63 with L26) for the semi-Lagrangian dynamical core (SLD). The increase in ITCZ rainfall at higher temporal resolution was primarily a result of the increase in large-scale precipitation. This increase in rainfall was caused by the positive feedback between surface evaporation, latent heating, and surface wind speed. Similar results were obtained when the semi-Lagrangian dynamical core was replaced by the Eulerian dynamical core. When the surface evaporation was specified, changes in rainfall were largely insensitive to temporal resolution. The impact of temporal resolution on rainfall was more sensitive to the latitudinal gradient of SST than to the magnitude of SST.
Abstract
Soil moisture is a very important boundary parameter in numerical weather prediction at different spatial and temporal scales. Satellite-based microwave radiometric observations are considered to be the best because of their high sensitivity to soil moisture, apart from possessing all-weather and day–night observation capabilities with high repetitousness. In the present study, 6.6-GHz horizontal-polarization brightness temperature data from the Multifrequency Scanning Microwave Radiometer (MSMR) onboard the Indian Remote Sensing Satellite IRS-P4 have been used for the estimation of large-area-averaged soil wetness. A methodology has been developed for the estimation of soil wetness for the period of June–July from the time series of MSMR brightness temperatures over India. Maximum and minimum brightness temperatures for each pixel are assigned to the driest and wettest periods, respectively. A daily soil wetness index over each pixel is computed by normalizing brightness temperature observations from these extreme values. This algorithm has the advantage that it takes into account the effect of time-invariant factors, such as vegetation, surface roughness, and soil characteristics, on soil wetness estimation. Weekly soil wetness maps compare well to corresponding weekly rainfall maps depicting clearly the regions of dry and wet soil conditions. Comparisons of MSMR-derived soil wetness with in situ observations show a high correlation (R > 0.75), with a standard error of the soil moisture estimate of less than 7% (volumetric unit) for the surface (0–5 cm) and subsurface (5–10 cm) soil moisture.
Abstract
Soil moisture is a very important boundary parameter in numerical weather prediction at different spatial and temporal scales. Satellite-based microwave radiometric observations are considered to be the best because of their high sensitivity to soil moisture, apart from possessing all-weather and day–night observation capabilities with high repetitousness. In the present study, 6.6-GHz horizontal-polarization brightness temperature data from the Multifrequency Scanning Microwave Radiometer (MSMR) onboard the Indian Remote Sensing Satellite IRS-P4 have been used for the estimation of large-area-averaged soil wetness. A methodology has been developed for the estimation of soil wetness for the period of June–July from the time series of MSMR brightness temperatures over India. Maximum and minimum brightness temperatures for each pixel are assigned to the driest and wettest periods, respectively. A daily soil wetness index over each pixel is computed by normalizing brightness temperature observations from these extreme values. This algorithm has the advantage that it takes into account the effect of time-invariant factors, such as vegetation, surface roughness, and soil characteristics, on soil wetness estimation. Weekly soil wetness maps compare well to corresponding weekly rainfall maps depicting clearly the regions of dry and wet soil conditions. Comparisons of MSMR-derived soil wetness with in situ observations show a high correlation (R > 0.75), with a standard error of the soil moisture estimate of less than 7% (volumetric unit) for the surface (0–5 cm) and subsurface (5–10 cm) soil moisture.
Soil moisture is an important variable in the climate system. Understanding and predicting variations of surface temperature, drought, and flood depend critically on knowledge of soil moisture variations, as do impacts of climate change and weather forecasting. An observational dataset of actual in situ measurements is crucial for climatological analysis, for model development and evaluation, and as ground truth for remote sensing. To that end, the Global Soil Moisture Data Bank, a Web site (http://climate.envsci.rutgers.edu/soil_moisture) dedicated to collection, dissemination, and analysis of soil moisture data from around the globe, is described. The data bank currently has soil moisture observations for over 600 stations from a large variety of global climates, including the former Soviet Union, China, Mongolia, India, and the United States. Most of the data are in situ gravimetric observations of soil moisture; all extend for at least 6 years and most for more than 15 years. Most of the stations have grass vegetation, and some are agricultural. The observations have been used to examine the temporal and spatial scales of soil moisture variations, to evaluate Atmospheric Model Intercomparison Project, Project for Intercomparison of Land-Surface Parameterization Schemes, and Global Soil Wetness Project simulations of soil moisture, for remote sensing of soil moisture, for designing new soil moisture observational networks, and to examine soil moisture trends. For the top 1-m soil layers, the temporal scale of soil moisture variation at all midlatitude sites is 1.5 to 2 months and the spatial scale is about 500 km. Land surface models, in general, do not capture the observed soil moisture variations when forced with either model-generated or observed meteorology. In contrast to predictions of summer desiccation with increasing temperatures, for the stations with the longest records summer soil moisture in the top 1 m has increased while temperatures have risen. The increasing trend in precipitation more than compensated for the enhanced evaporation.
Soil moisture is an important variable in the climate system. Understanding and predicting variations of surface temperature, drought, and flood depend critically on knowledge of soil moisture variations, as do impacts of climate change and weather forecasting. An observational dataset of actual in situ measurements is crucial for climatological analysis, for model development and evaluation, and as ground truth for remote sensing. To that end, the Global Soil Moisture Data Bank, a Web site (http://climate.envsci.rutgers.edu/soil_moisture) dedicated to collection, dissemination, and analysis of soil moisture data from around the globe, is described. The data bank currently has soil moisture observations for over 600 stations from a large variety of global climates, including the former Soviet Union, China, Mongolia, India, and the United States. Most of the data are in situ gravimetric observations of soil moisture; all extend for at least 6 years and most for more than 15 years. Most of the stations have grass vegetation, and some are agricultural. The observations have been used to examine the temporal and spatial scales of soil moisture variations, to evaluate Atmospheric Model Intercomparison Project, Project for Intercomparison of Land-Surface Parameterization Schemes, and Global Soil Wetness Project simulations of soil moisture, for remote sensing of soil moisture, for designing new soil moisture observational networks, and to examine soil moisture trends. For the top 1-m soil layers, the temporal scale of soil moisture variation at all midlatitude sites is 1.5 to 2 months and the spatial scale is about 500 km. Land surface models, in general, do not capture the observed soil moisture variations when forced with either model-generated or observed meteorology. In contrast to predictions of summer desiccation with increasing temperatures, for the stations with the longest records summer soil moisture in the top 1 m has increased while temperatures have risen. The increasing trend in precipitation more than compensated for the enhanced evaporation.
