Estimation of relative humidity profiles From INSAT cloud data

Accurate humidity profiles are needed for obtaining useful rainfall forecasts from numerical weather prediction models. In this context objective estimation of moisture profiles over ocean areas using satellite cloud data becomes important. For this purpose the fractional cloudiness data available from INSAT has been classified into different cloud categories depending on the total cloud amount and the levels at which the clouds have been present. Actual relative humidity profiles have been obtained using TEMP data of Port Blair (11 .6°N 92.7°E) and Minicoy (8,3°N, 72,9°E), Most frequently occurring relative humidity profile has been selected as being representative of humidity distribution in the vertical for a given cloud category. The preliminary results reported here show that these bogus relative humidity profiles could provide useful Information on moisture distribution in the vertical over the Indian Ocean.

Sunshine duration climatology and trends in association with other climatic factors over India for 1970-2006

Changes in sunshine duration in association with total cloud amount, rainy days and good visibility days over India were examined for 1970-2006. Climatologically, annual total sunshine duration over west Rajasthan and adjoining Gujarat is more than 3100 hours which is ideal for harnessing solar energy over these regions. The trend analysis indicates significant decrease in sunshine duration over the country for all months (except June) and the maximum decrease has taken place in January (-0.44 hour/decade) followed by December (-0.39 hour/decade). Seasonally, decline in sunshine hours is highest in winter and post monsoon (4% per decade) and lowest in monsoon (3% per decade). Decadal variations indicate maximum decrease in sunshine over the Indo-Gangetic plains and south peninsula during 1990-1999. Spatially, the decreasing trends in sunshine hours are highest in Indo-Gangetic plains and south peninsula while regions over Rajasthan and Gujarat have lowest decrease. Out of 40 stations under study, the maximum decrease in sunshine has occurred at New Delhi (winter at 13% per decade and post monsoon at 10% per decade) and Varanasi (summer and monsoon at 7% per decade). Correlation analysis of sunshine duration with total cloud amount, rainy days and good visibility days indicates regional and seasonal variations in factors explaining the long term trends in sunshine duration over the country.

Recent trends in meteorological parameters over Jammu and Kashmir

Annual trends of meteorological parameters temperature, rainfall, relative humidity and clouds for ten stations in Jammu and Kashmir during the period 1976-2007 were studied. Trend analysis shows that temperatures are increasing over the state with significant increase in maximum temperature in the Kashmir region (+0.04 to + 0.05° C/year) and minimum temperature in the Jammu region (+0.03 to + 0.08° C/year). The diurnal temperature range (DTR) is increasing over Kashmir region due to higher increasing trends in the maximum temperature while the strong increasing trends in the minimum temperature are contributing more towards the decrease in DTR over the Jammu region. Annual rainfall and rainy days trends are decreasing in both the regions of the state except at Jammu where rainfall trend is significantly increasing (+12.05 mm/year). Day-time relative humidity trends are mixed while total cloud amount trends are decreasing over Kashmir region and increasing over Jammu region. The effects of urbanization in the last two decades are more pronounced in Jammu region and this is strongly expressed in minimum temperature over the region. The warming trends observed over Jammu and Kashmir state during the period of study need further investigation in relation to variability of atmospheric circulation over North India.

Recent winter warming over India – spatial and temporal characteristics of monthly maximum and minimum temperature trends for January to March

In the backdrop of recent warmer winters over India, temperature series of 174 stations well distributed over the country were statistically analyzed to document the long term variations and trends in monthly mean maximum and minimum temperatures for January to March. From the trend analysis, February month has emerged as the warmer winter month over North India where increase in both maximum (+0.29° C / decade) and minimum (+0.38° C / decade) temperatures is highest with noteworthy increase in maximum temperature at a rate 1.5 times that of the South India averaged increase. Spatially, North India minimum temperature trends for February and March and South India maximum temperature trends for all months are more coherent. Both day-time and night-time total cloud amounts are increasing significantly over Indo-Gangetic plains and south peninsula and decreasing significantly in central and east India. However increase in temperatures over extreme south peninsula in January and March is difficult to explain on the basis of increase in day-time total cloud amount indicating strong influence of other climatic factors. At the same time, sea surface temperatures of the Arabian Sea and the Bay of Bengal are rising and there is strong positive correlation between land surface temperatures and sea surface temperatures suggesting significant contribution of warmer sea waters which may have important climatic implications over neighbouring regions.

International Satellite Cloud Climatology Project: Extending the Record

ISCCP continues to quantify the global distribution and diurnal-to-interannual variations of cloud properties in a revised version. This paper summarizes assessments of the previous version, describes refinements of the analysis and enhanced features of the product design, discusses the few notable changes in the results, and illustrates the long-term variations of global mean cloud properties and differing high cloud changes associated with ENSO. The new product design includes a global, pixel-level product on a 0.1°?grid, all other gridded products at 1.0°-equivalent equal-area, separate-satellite products with ancillary data for regional studies, more detailed, embedded quality information, and all gridded products in netCDF format. All the data products including all input data), expanded documentation, the processing code and an Operations Guide are available online. Notable changes are: (1) a lowered ice-liquid temperature threshold, (2) a treatment of the radiative effects of aerosols and surface temperature inversions, (3) refined specification of the assumed cloud microphysics, and (4) interpolation of the main daytime cloud information overnight. The changes very slightly increase the global monthly mean cloud amount with a little more high and a little less middle and low cloud. Over the whole period, total cloud amount slowly decreases caused by decreases in cumulus/altocumulus; consequently, average cloud top temperature and optical thickness have increased. The diurnal and seasonal cloud variations are very similar to earlier versions. Analysis of the whole record shows that high cloud variations, but not low clouds, exhibit different patterns in different ENSO events.

Estimating Sunshine Duration Using Hourly Total Cloud Amount Data from a Geostationary Meteorological Satellite

Sunshine duration is an important indicator of the amount of solar radiation received in a region and an important input parameter for the study of atmospheric energy balance, climate change, ecosystem evolution, and social sustainability. Currently, extrapolation and interpolation of data from meteorological stations are the most common methods used to calculate sunshine duration on a regional scale. However, it is difficult to obtain high precision sunshine duration in areas lacking ground observation or where sunshine duration is highly heterogeneous on the ground. In this paper, a new method is proposed to estimate sunshine duration with hourly total cloud amount (CTA) data from sunrise to sunset derived from the Fengyun-2G geostationary meteorological satellite (FY-2G). This method constructs a new index known as daytime mean total cloud coverage amount and provides quadratic equations relating daytime mean total cloud coverage amount to relative sunshine duration in different seasons. The method was validated with ground observation data for 2016 from 18 meteorological stations in the Three-River Headwaters Region of Qinghai Province, China. For individual stations, the coefficient of determination (R2) between estimated and measured sunshine was at least 0.894, the RMSE (root mean square error) was 0.977 h/day or less, the MAE (mean absolute error) was 0.824 h/day or less, the RE (relative error) was 0.150 or lower, and the value of d was 0.963 or greater, which validated that the proposed method can effectively predict daily sunshine duration. These equations can also provide higher precision estimates of regional-scale sunshine duration. This was demonstrated by comparing, for the entire study region, the spatial distribution of sunshine duration estimated from season-based equations with results from three different interpolation methods based on ground observations. Overall, the study confirms that total cloud amount measures from a geostationary satellite can be used to successfully estimate sunshine duration.

Elucidating ice formation pathways in the aerosol–climate model ECHAM6-HAM2

Cloud microphysics schemes in global climate models have long suffered from a lack of reliable satellite observations of cloud ice. At the same time there is a broad consensus that the correct simulation of cloud phase is imperative for a reliable assessment of Earth's climate sensitivity. At the core of this problem is understanding the causes for the inter-model spread of the predicted cloud phase partitioning. This work introduces a new method to build a sound cause-and-effect relation between the microphysical parameterizations employed in our model and the resulting cloud field by analysing ice formation pathways. We find that freezing processes in supercooled liquid clouds only dominate ice formation in roughly 6 % of the simulated clouds, a small fraction compared to roughly 63 % of the clouds governed by freezing in the cirrus temperature regime below −35 ∘C. This pathway analysis further reveals that even in the mixed-phase temperature regime between −35 and 0 ∘C, the dominant source of ice is the sedimentation of ice crystals that originated in the cirrus regime. The simulated fraction of ice cloud to total cloud amount in our model is lower than that reported by the CALIPSO-GOCCP satellite product. This is most likely caused by structural differences of the cloud and aerosol fields in our model rather than the microphysical parametrizations employed.

Arctic cloud annual cycle biases in climate models

Arctic clouds exhibit a robust annual cycle with maximum cloudiness in fall and minimum cloudiness in winter. These variations affect energy flows in the Arctic with a large influence on the surface radiative fluxes. Contemporary climate models struggle to reproduce the observed Arctic cloud amount annual cycle and significantly disagree with each other. The goal of this analysis is to quantify the cloud-influencing factors that contribute to winter–summer cloud amount differences, as these seasons are primarily responsible for the model discrepancies with observations. We find that differences in the total cloud amount annual cycle are primarily caused by differences in low, rather than high, clouds; the largest differences occur between the surface and 950 hPa. Grouping models based on their seasonal cycles of cloud amount and stratifying cloud amount by cloud-influencing factors, we find that model groups disagree most under strong lower tropospheric stability, weak to moderate mid-tropospheric subsidence, and cold lower tropospheric air temperatures. Intergroup differences in low cloud amount are found to be a function of lower tropospheric thermodynamic characteristics. Further, we find that models with a larger low cloud amount in winter have a larger ice condensate fraction, whereas models with a larger low cloud amount in summer have a smaller ice condensate fraction. Stratifying model output by the specifics of the cloud microphysical scheme reveals that models treating cloud ice and liquid condensate as separate prognostic variables simulate a larger ice condensate fraction than those that treat total cloud condensate as a prognostic variable and use a temperature-dependent phase partitioning. Thus, the cloud microphysical parameterization is the primary cause of inter-model differences in the Arctic cloud annual cycle, providing further evidence of the important role that cloud ice microphysical processes play in the evolution and modeling of the Arctic climate system.

Arctic cloud annual cycle biases in climate models

Arctic clouds exhibit a robust annual cycle with maximum cloudiness in fall and minimum in winter. These variations affect energy flows in the Arctic with a large influence on the surface radiative fluxes. Contemporary climate models struggle to reproduce the observed Arctic cloud amount annual cycle and significantly disagree with each other. The goal of this analysis is to quantify the cloud influencing factors that contribute to winter-summer cloud amount differences, as these seasons are primarily responsible for the model discrepancies with observations. We find that differences in the total cloud amount annual cycle are primarily caused by differences in low, not high, clouds; the largest differences occur between the surface and 950 hPa. Stratifying cloud amount by cloud influencing factors, we find that model groups disagree most under strong lower tropospheric stability, weak to moderate mid-tropospheric subsidence, and cold lower tropospheric air temperatures. Inter-group differences in low cloud amount are found to be a function of the dependence of low cloud amount on the lower tropospheric thermodynamic characteristics. We find that models with a larger low cloud amount in winter produce more cloud ice, whereas models with a larger low cloud amount in summer produce more cloud liquid. Thus, the parameterization of ice microphysics, specifically the ice formation mechanism (deposition vs. immersion freezing) and cloud liquid and ice partitioning, contributes to the inter-model differences in the Arctic cloud annual cycle and provides further evidence of the important role that cloud ice microphysical processes play in the evolution and modeling of the Arctic climate system.