Identification of key parameters producing rainfall

o"kkZ ds eq[; izkpyksa dk irk yxkus ds fy, bl 'kks/k i= ls 'kks/kdrkZvksa us m".kdfVca/kh; o"kkZ ekiu fe’ku ¼Vh- vkj- ,e- ,e-½ mixzg vk¡dM+k vk/kkj dh tk¡p dh gSA bl rF; dks le>us ds mijkUr fd c<+us okys ok;q iklZy ds }kjk ikuh ds ok"ihdj.k] ok"i ds la?kuu vkSj m"ek ÅtkZ ds laogu ls es?k cursa gS vkSj o"kkZ gksrh gSA 'kks/kdrkZvksa us ok;qeaMy dh fofHkUu Å¡pkbZ;ksa ij o"kkZ izfØ;k ds eq[; lg;ksfx;ksa ds :i esa es?k nzo ty ¼lh-,y-MCY;w-½] o"kZ.k ty ¼ih-MCY;w-½ rFkk xqIr m"ek ¼,y-,p-½ ds ckjs esa tkudkjh izkIr djuh vkjaHk dj nh gSSA bu vk¡dM+ksa dks cgq lekJ;.k fun’kZ esa Mkyk x;k gSA ;g ik;k x;k gSS fd o"kkZ vkSj bu izkpyksa esa egRoiw.kZ lglaca/k gSA blls LFkkfir gq, dk;kZRed laca/kksa ls fdlh Hkh le; o"kkZ dk vkdyu fd;k tk ldrk gS c’krZs dkWyeuj lh-,y-MCY;w-] ih-MCY;w- vkSj ,y-,p- eku miyC?k gksaA ,d ;k nks ds LFkku ij bu lHkh rhuksa izkpyksa dks cgq lekJ;.k fun’kZ esa 'kkfey djus ds QyLo:i o"kkZ dk csgrj iwokZuqeku yxk;k tk ldk gSA lh- ,y- MCY;w-] ,y- ,p- vkSj ih- MCY;w- ds chp egRoiw.kZ lglaca/k gSaA In search of the key parameters causing rainfall, the authors have explored Tropical Rainfall Measuring Mission (TRMM) satellite data base. By realizing the fact that evaporation of water, condensation of vapour and transport of heat energy by a rising air parcel are all about formation of cloud and rain, the authors have started their quest considering cloud liquid water (CLW), precipitation water (PW) and latent heat (LH) at different altitudes of the atmosphere as major contributors to rainfall mechanism. These data have been fitted to multiple regressions. It is found that significant correlations exist between rainfall and these parameters. The functional relationships so established are able to estimate surface rainfall at any instant, provided columnar CLW, PW and LH values are available. Inclusion of all the three parameters in multiple regression leads to better predictability of rainfall, instead of one or two. Significant correlations exist between CLW, LH and PW.

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PEMANFAATAN DATA SATELIT TROPICAL RAINFALL MEASURING MISSION (TRMM) UNTUK PEMETAAN ZONA AGROKLIMAT OLDEMAN DI KALIMANTAN SELATAN

EnviroScienteae ◽

10.20527/es.v12i3.2452 ◽

2016 ◽

Vol 12 (3) ◽

pp. 267 ◽

Cited By ~ 1

Author(s):

Riza Arian Noor ◽

Muhammad Ruslan ◽

Gusti Rusmayadi ◽

Badaruddin Badaruddin

Keyword(s):

Satellite Data ◽

Tropical Rainfall Measuring Mission ◽

Data Availability ◽

Observation Data ◽

Resources Management ◽

Agricultural Resources ◽

Monthly Average ◽

Surface Rainfall ◽

Calculation Results ◽

Arc Gis

The irregularity of observation sites distribution and network density, lack data availability and discontinuity are the obstacles to analyzing and producing the information of agroclimate zone in South Kalimantan. TRMM satellite needs to be researched to overcome the limitations of surface observation data. This study intended to validate TRMM 3B43 satellite data with surface rainfall, to produce Oldeman agroclimate zone based on TRMM satellite data and to analyze the agroclimate zone for agricultural resources management. Data validation is done using the statistical method by analyzing the correlation value (r) and RMSE (Root Mean Square Error). The agroclimate zone is classified based on Oldeman climate classification type. The calculation results are mapped spatially using Arc GIS 10.2 software. The validation result of the TRMM satellite and surface rainfall data shows a high correlation value for the monthly average. The value of correlation coefficient is 0,97 and 25 mm for RMSE value. Oldeman agroclimate zone based on TRMM satellite data in south Kalimantan is divided into five climate zones, such as B1, B2, C1, C2, and D1.

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Finding the Signature of Climate Change in Meteorological Elements: A Study of Cloud Liquid Water, Precipitation Water, Latent Heat, Temperature, and Rainfall

The International Journal of Climate Change Impacts and Responses ◽

10.18848/1835-7156/cgp/v08i03/1-27 ◽

2016 ◽

Vol 8 (3) ◽

pp. 1-27

Author(s):

Rajasri Sen Jaiswal ◽

Neela Vus ◽

Sonia Fredrick ◽

Rasheed Mohamed ◽

Leena Zaveri

Keyword(s):

Climate Change ◽

Latent Heat ◽

Liquid Water ◽

Heat Temperature ◽

Cloud Liquid Water ◽

Precipitation Water ◽

Meteorological Elements

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Study of the microphysical properties associated with the Monsoon Intraseasonal Oscillation as seen from the TRMM observations

Annales Geophysicae ◽

10.5194/angeo-30-897-2012 ◽

2012 ◽

Vol 30 (6) ◽

pp. 897-910 ◽

Cited By ~ 14

Author(s):

M. Halder ◽

P. Mukhopadhyay ◽

S. Halder

Keyword(s):

Summer Monsoon ◽

Indian Summer Monsoon ◽

Liquid Water ◽

Tropical Rainfall Measuring Mission ◽

Cloud Water ◽

Cloud Liquid Water ◽

Tropical Rainfall ◽

Intraseasonal Oscillations ◽

Cloud Ice

Abstract. The spatio-temporal variability of Indian Summer Monsoon is well studied based on different types of rainfall data. However, very few attempts have been made to study the underlying role of clouds and its hydrometeors on Monsoon Intraseasonal Oscillations. The northward propagating Monsoon Intraseasonal Oscillations and its characteristics remain a challenge for the numerical modelers even today. In view of this, we have set out to analyze the role of cloud hydrometeors and their linkage with northward propagating Monsoon Intraseasonal Oscillations. The science question that we intend to address here is whether the different phases of the cloud hydrometeors show similar propagation characteristics as that of rainfall, and what are the relations of their phases with the convection centre using Tropical Rainfall Measuring Mission data. In answering the question, we have analyzed ten years of Tropical Rainfall Measuring Mission 2A12 hydrometeor data over Indian region. Our analyses show that the cloud water and cloud ice do show a large scale organization during the Indian Summer Monsoon regime of June–September, and systematically progress northward getting initiated over equatorial Indian Ocean. On further analyses, we found that cloud water actually leads the rainfall and cloud ice lags the rainfall. We have further demonstrated the process by analyzing dynamical parameters from Modern Era-Retrospective Analysis for Research and Applications. The presence of cloud water in the lower troposphere in the leading edge of rainfall indicates the lower level moistening and preconditioning of the convective instability due to enhanced moisture convergence. Subsequently, deep convection is triggered, which generates hydrometeor above freezing level and cloud ice in the upper troposphere. To quantify objectively the relation among cloud liquid water, cloud ice and rainfall, the lag correlation is computed with respect to convection center, where the above hypothesis is established that cloud liquid water leads the rainfall and cloud ice lag. This relation among hydrometeors may help the numerical modelers to incorporate such processes for capturing the characteristics of Monsoon Intraseasonal Oscillations.

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Retrieval of Atmospheric Integrated Water Vapor and Cloud Liquid Water Content Over the Ocean From Satellite Data Using the 1-D-Var Ice Cloud Microphysics Data Assimilation System (IMDAS)

IEEE Transactions on Geoscience and Remote Sensing ◽

10.1109/tgrs.2007.907740 ◽

2008 ◽

Vol 46 (1) ◽

pp. 119-129 ◽

Cited By ~ 9

Author(s):

Cyrus Raza Mirza ◽

Toshio Koike ◽

Kun Yang ◽

Tobias Graf

Keyword(s):

Water Vapor ◽

Satellite Data ◽

Liquid Water ◽

Liquid Water Content ◽

Cloud Microphysics ◽

Cloud Liquid Water ◽

Ice Cloud ◽

Data Assimilation System ◽

Cloud Liquid Water Content ◽

Assimilation System

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Prediction of Rain on the Basis of Cloud Liquid Water, Precipitation Water and Latent Heat in the Perspective of Climate Change over Two Coastal Stations

Climate Change and Island and Coastal Vulnerability ◽

10.1007/978-94-007-6016-5_3 ◽

2013 ◽

pp. 31-52

Author(s):

Rajasri Sen Jaiswal ◽

S. V. Neela ◽

Sonia R. Fredrick ◽

M. Rasheed ◽

Leena Zaveri

Keyword(s):

Climate Change ◽

Latent Heat ◽

Liquid Water ◽

Cloud Liquid Water ◽

Precipitation Water

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A new airborne Ka-band double-antenna microwave radiometer for cloud liquid water content measurement

10.1117/12.2023023 ◽

2013 ◽

Author(s):

Jian Sun ◽

Kai Zhao ◽

Tao Jiang ◽

Lingjia Gu

Keyword(s):

Water Content ◽

Liquid Water ◽

Microwave Radiometer ◽

Liquid Water Content ◽

Cloud Liquid Water ◽

Ka Band ◽

Content Measurement ◽

Cloud Liquid Water Content

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Variational Assimilation of Cloud Liquid/Ice Water Path and Its Impact on NWP

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-14-0243.1 ◽

2015 ◽

Vol 54 (8) ◽

pp. 1809-1825 ◽

Cited By ~ 18

Author(s):

Yaodeng Chen ◽

Hongli Wang ◽

Jinzhong Min ◽

Xiang-Yu Huang ◽

Patrick Minnis ◽

...

Keyword(s):

Data Assimilation ◽

Liquid Water ◽

Weather Prediction ◽

Wrf Model ◽

Liquid Water Path ◽

Cloud Liquid Water ◽

Water Path ◽

Ice Water Path ◽

Ice Water ◽

The Impact

AbstractAnalysis of the cloud components in numerical weather prediction models using advanced data assimilation techniques has been a prime topic in recent years. In this research, the variational data assimilation (DA) system for the Weather Research and Forecasting (WRF) Model (WRFDA) is further developed to assimilate satellite cloud products that will produce the cloud liquid water and ice water analysis. Observation operators for the cloud liquid water path and cloud ice water path are developed and incorporated into the WRFDA system. The updated system is tested by assimilating cloud liquid water path and cloud ice water path observations from Global Geostationary Gridded Cloud Products at NASA. To assess the impact of cloud liquid/ice water path data assimilation on short-term regional numerical weather prediction (NWP), 3-hourly cycling data assimilation and forecast experiments with and without the use of the cloud liquid/ice water paths are conducted. It is shown that assimilating cloud liquid/ice water paths increases the accuracy of temperature, humidity, and wind analyses at model levels between 300 and 150 hPa after 5 cycles (15 h). It is also shown that assimilating cloud liquid/ice water paths significantly reduces forecast errors in temperature and wind at model levels between 300 and 150 hPa. The precipitation forecast skills are improved as well. One reason that leads to the improved analysis and forecast is that the 3-hourly rapid update cycle carries over the impact of cloud information from the previous cycles spun up by the WRF Model.

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Constraints on interactions between aerosols and clouds on a global scale from a combination of MODIS-CERES satellite data and climate simulations

Atmospheric Chemistry and Physics ◽

10.5194/acp-10-9851-2010 ◽

2010 ◽

Vol 10 (20) ◽

pp. 9851-9861 ◽

Cited By ~ 12

Author(s):

X. Ma ◽

K. von Salzen ◽

J. Cole

Keyword(s):

Liquid Water ◽

Negative Relationship ◽

Cloud Droplet ◽

Global Scale ◽

Effective Radius ◽

Liquid Water Path ◽

Cloud Droplets ◽

Cloud Liquid Water ◽

Albedo Effect ◽

Cloud Liquid Water Path

Abstract. Satellite-based cloud top effective radius retrieved by the CERES Science Team were combined with simulated aerosol concentrations from CCCma CanAM4 to examine relationships between aerosol and cloud that underlie the first aerosol indirect (cloud albedo) effect. Evidence of a strong negative relationship between sulphate, and organic aerosols, with cloud top effective radius was found for low clouds, indicating both aerosol types are contributing to the first indirect effect on a global scale. Furthermore, effects of aerosol on the cloud droplet effective radius are more pronounced for larger cloud liquid water paths. While CanAM4 broadly reproduces the observed relationship between sulphate aerosols and cloud droplets, it does not reproduce the dependency of cloud top droplet size on organic aerosol concentrations nor the dependency on cloud liquid water path. Simulations with a modified version of the model yield a more realistic dependency of cloud droplets on organic carbon. The robustness of the methods used in the study are investigated by repeating the analysis using aerosol simulated by the GOCART model and cloud top effective radii derived from the MODIS Science Team.

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