A comparison of solar radiative flux above clouds from MODIS with BALTIMOS regional climate model simulations

A comparison study for the solar radiative flux above clouds is presented between the regional climate model system BALTEX integrated model system (BALTIMOS) and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite observations. For MODIS, an algorithm has been developed to retrieve reflected shortwave fluxes over clouds. The study area is the Baltic Sea catchment area during an 11-month period from February to December 2002. The intercomparison focuses on the variations of the daily and seasonal cycle and the spatial distributions. We found good agreement between the observed and the simulated data with a bias of the temporal mean of 13.6 W/m² and a bias of the spatial mean of 35.5 W/m². For summer months, BALTIMOS overestimates the solar flux with up to 90 W/m² (20%). This might be explained by the insufficient representation of cirrus clouds in the regional climate model.     

A comparison of the BALTIMOS coupled climate model with atmospheric and sea surface parameters derived from AMSR-E

The BALTEX Integrated Model System (BALTIMOS) coupled atmosphere ocean model was compared to passive microwave observations of the Advanced Microwave Scanning Radiometer (AMSR-E). Emphasis was put on quantifying the uncertainties associated with the different variables based on data screening both in the model and observations. Monthly means of three atmospheric parameters, as well as sea surface temperature, were compared for a period of 1 year. Sea ice extent was also derived from AMSR-E and compared to the model data on a daily basis. It is shown that the accuracy of the comparisons on a monthly mean basis is limited by precipitation screening. Out of the three atmospheric parameters, surface wind speed and water vapor column amount agree with the model data to within the accuracy of the comparison. The vertically integrated cloud liquid water content diagnosed from BALTIMOS is systematically higher than the liquid water content derived from satellite, even if potential systematic errors are accounted for. In terms of coupling, the two most relevant variables discussed are sea surface temperature and sea ice extent. The temporal extent of sea ice in the investigation area is well represented, as are the periods of the main growing and decay periods. The total sea ice cover appears to be underestimated by BALTIMOS, especially in the peak season between January and the beginning of March. The amplitude of the annual cycle of sea surface temperature in BALTIMOS appears to be too weak compared to the observations, leading to too cold sea surface temperatures in summer and too warm sea surface temperatures in winter. This might also partially explain the underestimation of sea ice cover by BALTIMOS.     

Comparison of precipitation in the regional climate model BALTIMOS to radar observations

Observational data and simulations of the regional climate system Baltic integrated model system (BALTIMOS) were used to study precipitation in the Baltic Sea and its drainage basin with a special focus on the diurnal cycle. The study includes a general evaluation of BALTIMOS precipitation, showing that BALTIMOS has too many light rain events causing an overestimation of the total annual precipitation amount. The diurnal cycle as well as its spatial distribution was analysed. BALTIMOS captures the broad characteristics: a significant diurnal variability with an afternoon peak above land and weak variability with a nocturnal peak above sea. An algorithm to distinguish between frontal and convective precipitation was applied to examine the diurnal cycle more thoroughly. The local solar time of maximum rain in summer is about 1 to 2 h earlier in BALTIMOS than in radar observations of precipitation.     

Water vapour in BALTIMOS: a comparison with MODIS measurements

In this paper, we compare integrated water vapour (IWV) retrievals from the Moderate Resolution Spectrometer (MODIS) instrument on board the polar-orbiting Terra platform with those from the coupled regional climate model system BALTIMOS for a period of 2 years (October 2001 to October 2003). The comparison was made for the whole drainage basin of the Baltic Sea as well as major parts of Central Europe. The qualitative comparison between the two data sets of the integrated water vapour shows a good agreement. The patterns in the spatial distribution of the averaged integrated water vapour in both data sets are quite similar. However, significant differences occur in the Hungarian Lowlands, along the Po River and the Wallachia (Romania) in the order of 2.5 to 7.0 kg/m². For these areas, the BALTIMOS model is dryer than the MODIS observations. This could be an indication for the known summer drying effect of BALTIMOS but needs further investigations. The annual cycle as well as a diurnal developing of integrated water vapour from 09:00 to 12:00 UTC is well pronounced in both data sets. For both data sets, the overall annual variations are 17.5 kg/m². The observed overall diurnal variability are 1.4 kg/m² for MODIS and 1.04 kg/m² for BALTIMOS, respectively.     

Validation of Forecast Cloud Parameters from Multispectral AIRS Radiances

The well-known CO₂ slicing technique which provides retrievals of cloud parameters (effective height and amount) is adapted in light of model validation using multispectral infrared sounders. The technique is applied to both real Atmospheric Infrared Sounder (AIRS) radiances and to corresponding radiances simulated from global 6 h and 12 h forecasts for the 31 days of July 2008. The forecast model is the one used operationally at the Canadian Meteorological Centre. Radiances are simulated from the Radiative Transfer for the Television and Infrared Observation Satellite (TIROS) Operational Vertical Sounder (RTTOV) model. When compared to model output of cloud parameters, simulated retrievals help us understand systematic biases linked to the retrieval technique. Systematic errors of interest, attributed to forecast cloud parameters, are then more clearly assessed from real retrievals. This is the central idea of this paper. The proposed definition of model cloud top, based on cloud transmittance, corresponds well to the height derived from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) instrument. These lidar-derived cloud heights, in turn, confirm the nature of the biases produced by the CO₂ slicing technique (e.g., a negative bias increasing with height to about 2 km (approximately 50 hPa) for the highest clouds at 16 km (approximately 100 hPa)). Results suggest that the model has a tendency to produce an excess of low-level clouds below 2 km, compensated for by a deficit from 3 to 6 km. No significant differences are found between 6 h and 12 h forecast monthly fields, an indication that the model has sufficiently spun-up after a few hours. Retrieved global monthly cloud parameter fields are compared to independently derived products available from the Moderate Resolution Imaging Spectrometer (MODIS) and AIRS standard processing. Significant differences are noted, linked to the different retrieval approaches, input data and resolution. This is further evidence that, for validation purposes, definitions of observed and model parameters must be consistent.     

A New Application of Unsupervised Learning to Nighttime Sea Fog Detection

This paper presents a nighttime sea fog detection algorithm incorporating unsupervised learning technique. The algorithm is based on data sets that combine brightness temperatures from the 3.7 μm and 10.8 μm channels of the meteorological imager (MI) onboard the Communication, Ocean and Meteorological Satellite (COMS), with sea surface temperature from the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA). Previous algorithms generally employed threshold values including the brightness temperature difference between the near infrared and infrared. The threshold values were previously determined from climatological analysis or model simulation. Although this method using predetermined thresholds is very simple and effective in detecting low cloud, it has difficulty in distinguishing fog from stratus because they share similar characteristics of particle size and altitude. In order to improve this, the unsupervised learning approach, which allows a more effective interpretation from the insufficient information, has been utilized. The unsupervised learning method employed in this paper is the expectation–maximization (EM) algorithm that is widely used in incomplete data problems. It identifies distinguishing features of the data by organizing and optimizing the data. This allows for the application of optimal threshold values for fog detection by considering the characteristics of a specific domain. The algorithm has been evaluated using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) vertical profile products, which showed promising results within a local domain with probability of detection (POD) of 0.753 and critical success index (CSI) of 0.477, respectively.     

Comparisons of Two Cloud-Detection Schemes for Infrared Radiance Observations

The cloud-detection procedure developed by McNally and Watts （MW03） was added to the Weather Research and Forecasting Data Assimilation System. To provide some guidelines for setting up cloud-detection schemes, this study compares the MW03 scheme to the Multivariate and Minimum Residual （MMR） scheme for both simulated and real Advanced Infrared Sounder （AIRS） radiances. Results show that there is a high level of consistency between the results from simulated and real AIRS data. As expected, both cloud-detection schemes perform well in finding the cloud-contaminated channels based on the channels＇ peak levels. The cloud- detection results from MW03 are sensitive to the prescribed brightness temperature innovation threshold and brightness temperature gradient threshold. When increasing the brightness temperature innovation threshold for MW03 to roughly eight times the default threshold, the two cloud-detection schemes produce consistent data rejection distributions overall for high channels. MMR generally retains more data for long-wave channels. For both cloud-detection schemes, there is a high level of consistency between the cloud-free pixels and the visible/near-IR （Vis/NIR） cloud mask.     

Validation of BALTIMOS precipitation structures using BALTRAD radar data

The comparison of rain areas observed by the radar network BALTRAD and those produced by the model BALTIMOS shows that the model is able to reproduce the rain areas’ basic properties. These are power law distributed frequency distributions as well as lognormal rain area size distributions. The parameters governing these distributions are also matched by power laws evident for the observations and the BALTIMOS data. The distributions also provide evidence that the model produces too many large structures and too little small ones. However, the shape of these structures, represented by their fractal dimension, is well met. The perimeter of the model structures is only slightly smoother than that of the observed ones. The model is also able to reproduce the diurnal cycle of convective activity with emerging and decaying convective cells, though some deficiencies in the timing and the magnitude of the maximum occurrence of rain areas and peaks are evident. Additionally, it was found that the methods developed within this project are a valuable tool to validate BALTIMOS and potentially also other regional climate models.     

A note on: Estimation of rainfall due to squall lines over West Africa using meteosat imagery

Summary The results of the first step of a project to develop a method to estimate precipitation over the Soudano-Sahelian belt of West Africa are reported.The study has been performed over the period from 10 June to 9 July 1986 using hourly METEOSAT infrared images. 122 individual cloud clusters associated with squall lines or tropical storms have been tracked. For each event, the time variations of a convection index giving the volume of cloud cooler than –40°C has been determined every hour. The convection index exhibits a strong diurnal cycle. From daily rainfall amounts obtained at about 300 stations, and assuming a time apportion of rainfall within a cloud cluster, the time variations of the hourly total rainfall produced by the cluster can be determined and represents the precipitation index. Because of insufficient rainfall, the precipitation index has been determined for only 17 events. For 2/3 of the 17 cases, there is a significant correlation between the two indices. For each of the 17 events, precipitation has been regressed on the associated convective index and relative time variations. In that case, the results indicate that a convective index representing the life history of the cloud cluster can be calibrated with corresponding raingage measurements provided raingage data are available. Then, estimation of rainfall due to that event over data void regions can be obtained. However, this study shows that no universal relationship exists between precipitation: no rainfall can be estimated if there is not enough raingage measurements to construct a precipitation index for a tracked cloud cluster. This represents a limitation to the method.With 9 Figures     

Description and evaluation of the bergen climate model: ARPEGE coupled with MICOM

A new coupled atmosphere–ocean–sea ice model has been developed, named the Bergen Climate Model (BCM). It consists of the atmospheric model ARPEGE/IFS, together with a global version of the ocean model MICOM including a dynamic–thermodynamic sea ice model. The coupling between the two models uses the OASIS software package. The new model concept is described, and results from a 300-year control integration is evaluated against observational data. In BCM, both the atmosphere and the ocean components use grids which can be irregular and have non-matching coastlines. Much effort has been put into the development of optimal interpolation schemes between the models, in particular the non-trivial problem of flux conservation in the coastal areas. A flux adjustment technique has been applied to the heat and fresh-water fluxes. There is, however, a weak drift in global mean sea-surface temperature (SST) and sea-surface salinity (SSS) of respectively 0.1 °C and 0.02 psu per century. The model gives a realistic simulation of the radiation balance at the top-of-the-atmosphere, and the net surface fluxes of longwave, shortwave, and turbulent heat fluxes are within observed values. Both global and total zonal means of cloud cover and precipitation are fairly close to observations, and errors are mainly related to the strength and positioning of the Hadley cell. The mean sea-level pressure (SLP) is well simulated, and both the mean state and the interannual standard deviation show realistic features. The SST field is several degrees too cold in the equatorial upwelling area in the Pacific, and about 1 °C too warm along the eastern margins of the oceans, and in the polar regions. The deviation from Levitus salinity is typically 0.1 psu – 0.4 psu, with a tendency for positive anomalies in the Northern Hemisphere, and negative in the Southern Hemisphere. The sea-ice distribution is realistic, but with too thin ice in the Arctic Ocean and too small ice coverage in the Southern Ocean. These model deficiencies have a strong influence on the surface air temperatures in these regions. Horizontal oceanic mass transports are in the lower range of those observed. The strength of the meridional overturning in the Atlantic is 18 Sv. An analysis of the large-scale variability in the model climate reveals realistic El Niño – Southern Oscillation (ENSO) and North Atlantic–Arctic Oscillation (NAO/AO) characteristics in the SLP and surface temperatures, including spatial patterns, frequencies, and strength. While the NAO/AO spectrum is white in SLP and red in temperature, the ENSO spectrum shows an energy maximum near 3 years.     

The atmospheric boundary layer structure over the open and ice-covered Baltic Sea: in situ measurements compared to simulations with the regional model REMO

The regional model REMO, which is the atmospheric component of the coupled atmosphere–ice–ocean–land climate model system BALTIMOS, is tested with respect to its ability to simulate the atmospheric boundary layer over the open and ice-covered Baltic Sea. REMO simulations are compared to ship, radiosonde, and aircraft observations taken during eight field experiments. The main results of the comparisons are: (1) The sharpness and strength of the temperature inversion are underestimated by REMO. Over open water, this is connected with an overestimation of cloud coverage and moisture content above the inversion. (2) The vertical temperature stratification in the lowest 200 m over sea ice is too stable. (3) The horizontal inhomogeneity of sea ice concentration as observed by aircraft could not be properly represented by the prescribed ice concentration in REMO; large differences in the surface heat fluxes arise especially under cold-air advection conditions. The results of the comparisons suggest a reconsideration of the parameterization of subgrid-scale vertical exchange both under unstable und stable conditions.     

Characteristics of UV radiation at Tazhong of the Tarim Basin,west China

Tazhong station, located at the hinterland of the Taklimakan Desert in northwest China, experiences frequent dusty weather events during spring and summer seasons (its dusty season) caused by unstable stratified atmosphere, abundant sand source and strong low-level wind. On average, it has 246.2 dusty days each year, of which 16.2 days are classified as sand and dust storm days. To better understand the characteristic of solar ultraviolet (UV) radiation and factors influencing its variations under such an extreme environment, UV radiation data were collected continuously from 2007 to 2011 at Tazhong station using UVS-AB-T radiometer by Kipp and Zonen. This study documents observational characteristics of the UV radiation variations observed during the five-year period. Monthly UV radiation in this region varied in the range of 14.1–37.8 MJ m^?2 and the average annual amount was 320.7 MJ m^?2. The highest value of UV radiation occurred in June (62.5 W m^?2) while the lowest one in December (29.3 W m^?2). It showed a notable diurnal cycle, with peak value at 12:00–13:00 LST. Furthermore, its seasonal variation exhibited some unique features, with averaged UV magnitude showing an order of summer > spring > autumn > winter. The seasonal values were 37.0, 29.1, 24.9 and 15.9 MJ m^?2, respectively. In autumn and winter, its daily variations were relatively weak. However, significant daily variations were observed during spring and summer associated with frequent dust weather events occurring in the region. Further analysis showed that there was a significant correlation between the UV radiation and solar zenith angle under different weather conditions. Under the same solar zenith angle, UV radiation was higher during clear days while it was lower in sand and dust storm days. Our observations showed that there was a negative correlation between UV radiation and ozone, but such a relationship became absent in dusty days. The UV radiation was reduced by 6 % when cloud amount was 1–4 oktas, by 12 % when the cloud amount was 5–7 oktas, and by 24 % when the cloud amount was greater than 8 oktas. The relative reduction of UV radiation reached 26, 38, and 45 % in dust day, blowing sand day and sand and dust storm day, respectively. The results revealed that decrease in UV radiation can be attributed to cloud coverage and dust aerosols. Moreover, the reduction of UV radiation caused by dust aerosols was about 2–4 times greater than that caused by cloud coverage. These observational results are of value for improving our understanding of processes controlling UV radiation over sand desert and developing methods for its estimation and prediction.     

Daily precipitation forecast of ECMWF verified over Iran

In this paper, the performance of the Centre for Medium Range Weather Forecast (ECMWF) model (t?+?27 h to t?+?51 h) in predicting precipitation is discussed. This model is the first, which has been verified over Iran. The spatial resolution of the model is 0.351° and the 24-h forecasts are compared with daily observations. The study concentrates on year 2001 and the precipitation measurements were collected from the data of 2,048 rain gauges in Iran. The accuracy of four different interpolation methods (nearest neighborhood, inverse distance, kriging, and upscaling) was investigated. Using cross-validation, the inverse distance method (IDM) with minimum mean error was applied. Verification results are given in terms of difference fields (mean error?=?0.46 mm/day), rank–order correlation coefficients (0.70), as well as accuracy scores (false alarm ratio?=?0.50 and probability of detection?=?0.60) and skill scores (true skill statistics [TSS]?=?0.45) in year 2001. The position of the rain band was only partly captured by the ECMWF model; however, the position of maximum precipitations agrees with the observations well. The results show that the high values of TSS are associated with the large amounts of precipitation (over 25 mm). Slight to moderate precipitation events have been underforecasted by the model (bias?<?1) and it leads to a small value of TSS for these thresholds (5–25 mm/day). The ECMWF model has better performance in high and mountainous regions than over flat terrain and in deserts. Comparing TSS over the Alborz and the Zagros Mountains, it is obvious that the ability of the model to predict the convective precipitation events needs some improvement. The amount of daily precipitation has been also slightly overestimated over Iran. From the beginning of January up to 21 March 2001, the ECMWF time series indicates an obvious phase shift of 1 day, although in other months, no phase shift is noticed.     

风云二号卫星的冰云光学厚度反演偏差分析

冰云的微物理特性参数反演是云参数反演的难点和热点问题,目前风云二号(FY-2)卫星还没有相关的业务产品。考虑薄卷云覆盖在中低云上的两层云情况,采用六棱柱形状的冰云,在云相态识别基础上,利用FY-2 卫星观测数据,采用双通道算法反演冰云光学厚度。选取2013年8月的EOS/Terra和EOS/Aqua云参数产品对反演的FY-2云光学厚度精度进行比对分析。研究结果表明,联合FY-2的可见光通道和中波红外通道可反演冰云光学厚度。基于匹配得到的34个分析个例,FY-2反演的云光学厚度分布态势与EOS/ MODIS云产品相同,但FY-2云光学厚度反演值小于EOS/MODIS 云光学厚度产品值。FY-2 反演云光学厚度与EOS/MODIS云光学厚度产品的平均偏差为6.41,相关系数平均为0.92,线性拟合平均斜率为0.74。FY-2 与EOS/MODIS云光学厚度值偏差出现原因除了反演算法存在差异外,与反演所用数据的不同存在密切关系,基础观测数据越相近,FY-2 与EOS/MODIS云光学厚度反演结果的偏差越小。     

A physically‐based algorithm for estimating the relationship between aerosol mass and cloud droplet number

In this study, we present a relationship between total accumulation mode aerosol mass concentrations and cloud droplet number concentrations ( N _d). The fundamental aim with the present method is to arrive at a physically‐based conversion algorithm in which each step in the conversion is based on real physical processes that occur and can be observed in the atmosphere, and in which all of the fields involved can be observed or modeled. In the last conversion (the critical part in the algorithm), we use measurements of the size distributions of cloud droplet residual particles for different pollution conditions. This conversion assumes that the size of the residual particles can be described with a lognormal distribution function and uses the Hatch–Choate relationship to convert between residual volume and number. The relatively sparse data set with which we have developed the present algorithm results in a course classification of the aerosol mass field. Consequently, uncertainties need to be recognized when using the algorithm in its present form in model calculations. The algorithm has been used on data from 15 days and the agreement between calculated and observed N _d values is, with one exception, within a factor of 2 and for many of these cases also much better than a factor of 2. In addition to the results of the algorithm itself, we also present a least‐squares fit to the predicted N _d values. To improve the algorithm in the longer‐term requires more data of scavenging fractions, particle chemical composition and density, and residual particle size distributions as a function of aerosol mass loading and cloud type.     

利用NOAA-AVHRR观测数据反演云辐射特性的一种迭代方法

在可见光谱区 ,云的反射函数主要依赖于云的光学厚度 ;在近红外和中红外光谱区 ,云的反射函数主要依赖于云滴有效半径。根据上述原理 ,我们开发了一个利用NOAA AVHRR观测资料同时反演云的光学厚度和云滴有效半径的迭代方案。将该方案用于分析中国东海上空冬季层积云的辐射特性 ,取得了较为合理的结果。     

Solar Radiation Climatology of Alaska

Summary There are only six locations in Alaska for which global radiation data of more than a year in duration are available. This is an extremely sparse coverage for a state which covers 1.5×10&⁶ km² and stretches over at least three climatic zones. Cloud observations are, however, available from 18 stations. We used fractional cloud cover and cloud type data to model the global radiation and thus obtain a more complete radiation coverage for Alaska. This extended data set allowed an analysis of geographic and seasonal trends. A simple 1-layer model based on Haurwitz’s semi-empirical approach, allowing for changes in cloud type and fractional coverage, was developed. The model predicts the annual global radiation fluxes to within 2–11% of the observed values. Estimated monthly mean values gave an average accuracy within about 6% of the measurements. The estimates agree well with the observations during the first four months of the year but less so for the last four. Changing surface albedo might explain this deviation. Previously, the 1993 National Solar Radiation Data Base (NSRDB) from the National Renewable Energy Laboratory (NREL) modeled global radiation data for 16 Alaskan stations. Although more complete and complex, the NREL model requires a larger number of input parameters, which are not available for Alaska. Hence, we believe that our model, which is based on cloud-radiation relationship and is specifically tuned to Alaskan conditions, produces better results for this region. Annual global solar radiation flux measurements are compared with results from global coverage models based on the International Satellite Cloud Climatology Project (ISCCP) data. Contour plots of seasonal and mean annual spatial distribution of global radiation for Alaska are presented and discussed in the context of their climatic and geographic settings. Received July 16, 1997 Revised May 18,1998     

利用FY-2C卫星数据反演云辐射特性

本文利用FY-2C静止卫星提供的可见光、中红外和热红外观测数据, 开展了水云光学厚度、粒子有效半径和云顶温度的云参数遥感探测理论和反演方法研究。基于FY-2C可见光、中红外(3.75 μm)与热红外(11 μm)通道辐射率对云光学厚度、 云滴有效半径、云顶温度辐射参数的敏感性分析, 提出三通道同时反演云的光学厚度、云滴有效半径及云顶温度的迭代方案; 通过个例分析进行了云参数反演试验, 并将结果与MODIS的云反演产品进行了对比, 最后对反演误差进行了分析。主要结论如下:(1) 个例反演得到的云参数与各通道探测数据有着较好的对应关系, 迭代计算标准偏差在允许的计算精度范围内(<0.89%), 反演结果具有合理性; (2) 通过与MODIS云反演产品的对比可以看到, 两者云光学厚度、云滴有效半径的均值和直方图分布都非常一致, 而MODIS的云顶温度比FY-2C反演值要高, 考虑到FY-2C的 11 μm通道测量的辐射值与MODIS相比偏小, 因此认为我们的反演方法与MODIS方法的精度是相当的。     

An algorithm for generating stochastic cloud fields from radar profile statistics

An algorithm is described for generating stochastic three-dimensional (3D) cloud fields from time–height fields derived from vertically pointing radar. This model is designed to generate cloud fields that match the statistics of the input fields as closely as possible. The major assumptions of the algorithm are that the statistics of the fields are translationally invariant in the horizontal and independent of horizontal direction; however, the statistics do depend on height. The algorithm outputs 2D or 3D stochastic fields of liquid water content (LWC) and (optionally) effective radius. The algorithm is a generalization of the Fourier filtering methods often used for stochastic cloud models. The Fourier filtering procedure generates Gaussian stochastic fields from a “Gaussian” cross-correlation matrix, which is a function of a pair of heights and the horizontal distance (or “lag”). The Gaussian fields are nonlinearly transformed to give the correct LWC histogram for each height. The “Gaussian” cross-correlation matrix is specially chosen so that, after the nonlinear transformation, the cross-correlation matrix of the cloud mask fields approximately matches that derived from the input LWC fields. The cloud mask correlation function is chosen because the clear/cloud boundaries are thought to be important for 3D radiative transfer effects in cumulus.The stochastic cloud generation algorithm is tested with 3 months of boundary layer cumulus cloud data from an 8.6-mm wavelength radar on the island of Nauru. Winds from a 915-MHz wind profiler are used to convert the radar fields from time to horizontal distance. Tests are performed comparing the statistics of 744 radar-derived input fields to the statistics of 100 2D and 3D stochastic output fields. The single-point statistics as a function of height agree nearly perfectly. The input and stochastic cloud mask cross-correlation matrices agree fairly well. The cloud fractions agree to within 0.005 (the total cloud fraction is 18%). The cumulative distributions of optical depth, cloud thickness, cloud width, and intercloud gap length agree reasonably well. In the future, this stochastic cloud field generation algorithm will be used to study domain-averaged 3D radiative transfer effects in cumulus clouds.     

Lidar remote sounding of cirrus clouds and comparison of simulated fluxes with surface and METEOSAT observations

Three cirrus cloud cases have been remotely sounded near Paris by a ground-based backscatter lidar and broadband radiometers. Some cirrus properties (optical depth, emissivity, height) are derived from these measurements and used to compare radiative transfer calculations to surface and METEOSAT observations of broadband irradiances.For a useful comparison, the three cirrus cases were selected to have different morphologies and optical properties: June 29, 1993—thin cirrus cloud (thickness 1.5 km, optical depth 0.22); September 6, 1993—thick cirrus cloud (thickness 5 km, optical depth 2.7); and November 16, 1993—inhomogeneous and geometrically thick cirrus cloud (thickness 3.5–6.5 km) but optically thin (optical depth 0.82).At surface, the differences between measurements and model range from 1.5 to 4 Wm⁻² for longwave fluxes, and from 20 to 70 Wm⁻² for shortwave fluxes.At the top of the atmosphere, the differences between METEOSAT measurements and model are in fair agreement for longwave fluxes (up to 50 Wm⁻²). However, unexpected high differences are found for shortwave fluxes (up to 144 Wm⁻²) due to cirrus clouds heterogeneities and uncertainties in their microphysical properties and especially the occurrence of high reflectivity due to horizontally oriented ice crystals at the cloud top, which are not taken into account by the Model presently.