Inter annual,spatial, seasonal,and diurnal variability of precipitable water vapour over northeast India using GPS time series

We present multi-scale variability of GPS-derived column integrated precipitable water vapour (PWV) estimated over five continuous GPS sites of northeast India from 2004 to 2012. PWV is estimated from GPS-derived zenith total delay using observed surface pressure and temperature from collocated meteorological sensors as well as obtained by interpolating European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis project (ERA-Interim) global reanalysis dataset. PWV estimated using ERA-Interim-derived parameters compare well with the PWV estimated using observed meteorological parameters with bias of less than ± 0.1 mm and highest root mean square error of 0.56 mm. The average PWV for the study period is about 17 mm at Bomdila in the Eastern Himalayas, about 20 mm at Shillong in Shillong plateau, about 31 mm at Lumami in Arokan-Yoma Hill ranges, and about 43 mm at Guwahati and Tezpur in Assam valley. The high altitude sites show low annual PWV variability (around 49%) than the low altitude sites (around 63–67%). Seasonal PWV value coincides with the monsoon with maximum in summer and minimum in the winter. However, percentage seasonal PWV variability is found to be almost same (around 68%) for all the five sites. The Assam valley sites do not show a distinct diurnal cycle whereas the high altitude sites indicate a distinct diurnal cycle coinciding with the daily solar cycle. Insights in to GPS PWV variability and rainfall are presented for the study period.     

GPS-based precipitable water vapour retrieval and variability using measured and global reanalysis data in the coastal regions of China

ABSTRACT

The atmospheric water vapour is an important indicator of the climate state and evolution, and also the key parameter affecting the hydrological cycle, atmospheric convection, and the weather. This study validated the accuracy of surface pressure and temperature data interpolated from European Centre for Medium-Range Weather Forecasts (ECMWF) Interim ReAnalysis (ERA-Interim) and ECMWF 5th ReAnalysis (ERA5) datasets, and evaluated the performance of the interpolation meteorological data for precipitable water vapour (PWV) retrieval using the measured meteorological data and Global Positioning System (GPS) data of the year 2014 obtained from 25 stations of the Chinese Coastal GPS Observation Network. We then analysed the temporal and spatial distribution of water vapour in the coastal regions of China using a 3-year GPS PWV time series calculated with the interpolated reanalysis meteorological data at 25 stations from 2014 to 2016. Evaluation of the reanalysis meteorological data results showed that the accuracies of the interpolated pressure and temperature data from ERA5 were slightly superior than those from ERA-Interim. The root-mean-square errors (RMSEs) of the surface temperature and pressure interpolated from two reanalysis datasets were less than 2.4 K and 1.6 hPa, respectively. Data based on GPS PWV products that used interpolated parameters were very close to those of meteorological observations, with biases within ±0.4 mm and RMSEs below 0.5 mm in most areas; these data also strongly agreed with radiosonde observations. However, the interpolated meteorological data from reanalysis datasets could not reflect the true change during typhoon events, which could not be used in GPS PWV retrieval. The analysis of the temporal and spatial distribution showed that the distribution of water vapour was mainly affected by latitude, land-sea distribution, and water vapour advection. The variations in water vapour were mainly seasonal with the highest PWV occurring in the summer and the lowest always occurring in the winter. Significant diurnal variations of water vapour varied with season and latitude, with an amplitude of about 0.9–3.5 mm; the peak value occurred in the afternoon or early morning, and was affected by surface evaporation, large-scale land–sea breeze circulation, and valley-wind circulation.     

Studies of precipitable water vapour characteristics on a global scale

Atmospheric water vapour plays an important role in hydrological, global climate change, atmospheric, and meteorological processes. In this study, precipitable water vapour (PWV) data set for 2004–2017 was first estimated with an average accuracy of about 1.28 mm globally using the products provided by the International Global Navigation Satellite System Service and Global Geodetic Observation System Atmosphere and then the spatio-temporal trends of PWV variation were characterized. Periodic signals of the annual, semi-annual, and seasonal variations of PWV time series were detected based on the Lomb–Scargle periodogram and analysed by dividing the whole world into five geographical zones. From a global perspective, the average PWV has an increasing trend, which may be caused by global warming effects and anthropogenic activities. Analysis of different PWV amplitudes also shows that the main component of the PWV is annual amplitude except in low latitude zones. In addition, the PWV differences between weekends and weekdays for four seasons are also analysed globally, and the result indicates that the weekend effects caused by anthropogenic activity depend on season and region     

Dynamical features of GPS PWV variation associated with lightning activity

Water vapour is one component that causes spatial and temporal fluctuations in the lower atmosphere, which, in turn, introduce time delays into the global positioning system (GPS) signal. These delays can be exploited to estimate the precipitable water vapour (PWV), which is beneficial for meteorological applications. Because the vertical transfer of warm air to the troposphere triggers instability and atmospheric charges, lightning phenomena can fundamentally affect the GPS signals through changes in water vapour. From this hypothesis, GPS-derived PWV data have been proposed as a tool for monitoring lightning activity. In this paper, the variation of PWV in days with lightning at four selected stations in Peninsular Malaysia during the past two inter-monsoon events in May and November 2009 was observed. To verify the response, a peak alignment method was proposed with regard to the lightning with more than 100 discharge events and lightning days from 07:00 to 20:00 LT. A total of 66 lightning series were assessed, and PWV was observed to increase by approximately 2.46 mm and decrease by 4.16 mm before and after the peak point, respectively, when compared to fair-weather conditions. Approximately 62% of the lightning start times were concentrated within a period of less than 1 h before or after the PWV reached a maximum peak, 24% were observed between 1–2 h, and 14% started after 2 h. This trend implies that the use of GPS PWV data was more consistent and robust for the detection of lightning activity compared to the use of surface temperature and relative humidity data.     

Estimation of the precipitable water from the IR channel of the geostationary satellite

It has been shown that over the tropical oceans the total precipitable water can be estimated from the infrared radiometer data (10.5–12.5 μm) of the Geostationary Meteorological Satellite. The satellite derived values are in good agreement with the radiosonde data, with a 0.53 g/cm² rms difference. The weekly mean distribution of the precipitable water over the tropical ocean of western Pacific has been obtained in both summer and winter seasons. The pattern of this distribution is in reasonable agreement with other authors and climatological data.     

Geostatistical downscaling of AMSR2 precipitation with COMS infrared observations

This article presents a geostatistical approach for downscaling precipitation products from passive microwave satellites with geostationary Meteorological Satellite observations. More precisely, the Advanced Microwave Scanning Radiometer 2 (AMSR2) precipitation (daily level 3 product) with 0.25° spatial resolution and the Communication, Ocean and Meteorological Satellite (COMS) infrared (IR) data with 5 km spatial resolution were used for the downscaling experiment over the Korean peninsula. Brightness temperature data observed at COMS IR 1 and water vapour channels were incorporated for downscaling via area-to-point residual Kriging with non-linear regression. The evaluation results with densely sampled Automatic Weather Station data revealed that incorporating the COMS IR observations with the AMSR2 precipitation showed similar error statistics to those of the AMSR2 precipitation because of the limitations of IR observations themselves and the inherent errors of the AMSR2 precipitation product over land. However, the area-based evaluation using information entropy indicated that incorporating the COMS observations resulted in more detailed spatial variation in the final product than direct downscaling of the AMSR2 precipitation. In addition, local precipitation patterns could be captured when there were regions with missing precipitation values in the AMSR2 product. Consequently, the downscaling result is useful for understanding the local precipitation patterns with an accuracy similar to that provided by microwave satellite observations. It is also suggested that the spatial variability in the downscaling result and errors in input low-resolution data should be considered when downscaling coarse resolution data using fine resolution auxiliary variables.     

Multi‐layer perceptron neural network based algorithm for estimating precipitable water vapour from MODIS NIR data

This Letter presents a multi‐layer perceptron neural network (MLP‐NN) based algorithm to quantitatively determine precipitable water vapour (PWV) directly from near infrared (NIR) radiance measured by the Moderate Resolution Imaging Spectroradiometer (MODIS). First, the background of the MLP‐NN based algorithm is discussed briefly. Then, the radiance of MODIS NIR channels simulated through a radiative transfer model with a set of input variables covering a broad range of surface reflectance and water vapour content are used to train MLP‐NN. Finally, PWV values derived by the MLP‐NN based algorithm are compared with radiosonde observations and a root mean squared error of 5.2 kg m^?2 is found from this comparison.     

Evaluation of MODIS water vapour products over China using radiosonde data

Radiosonde data collected from 83 stations in China from January to December 2012 were used to evaluate Moderate Resolution Imaging Spectroradiometer (MODIS) near-infrared (NIR) and thermal infrared (IR) total precipitable water vapour (PWV) products. The results indicate that MODIS NIR PWV products shows better agreement with radiosonde data than with IR PWV products, with the correlation coefficients up to 0.95. The root mean square errors (RMSEs) of NIR PWV range from 2 to 8 mm with different stations, which shows significant regional differences over China. The mean RMSE is about 5.03 mm (~35%) with a positive deviation of 2.56 mm (~18%), indicating the occurrence of a slight overestimation. Moreover, MODIS IR PWV during night-time has a better agreement with radiosonde PWV than that during daytime. The mean RMSE of IR PWV during daytime was ~6.02 mm (~42%), with a positive deviation of 1.54 mm (~11%). The mean RMSE of IR PWV during night-time was ~5.81 mm (~40%), with a negative deviation of approximately ?0.04 mm (~0.25%). Both the NIR and IR PWV products during daytime tend to be higher than radiosonde PWV.     

Comparison of precipitable water over Hawaii using AVHRR-based split-window techniques,GPS and radiosondes

Precipitable water vapour (PWV) was estimated over Lihue, Kauai, from AVHRR data using split-window techniques. The predicted values using the satellite sensor data were compared to precipitable water vapour amounts obtained from radiosondes and corrected GPS measurements. Compared to the corrected GPS precipitable water, the Dalu and RV satellite methods had rms errors of 7.3 and 3.8 mm, respectively. Typical values of PWV over Hawaii are approximately 27.5 mm, suggesting errors of about 14% in values estimated using the satellite split window technique near Hawaii.     

Retrieval and analysis of precipitable water vapour based on GNSS,AIRS, and reanalysis models over Nigeria

Precipitable water vapour (PWV) retrieved from ground-based Global Navigation Satellite System (GNSS) stations over Nigeria from 2013 to 2014 is compared with PWV from a satellite remote-sensing technique (Atmospheric Infrared Sounder [AIRS]) and a global reanalysis model (ERA-Interim) over Nigeria. The PWV for AIRS and ERA-Interim was obtained from the respective data providers. PWV estimates from the different techniques were grouped into daily estimates and were matched and re-grouped into monthly and seasonal averages. The performance of the PWV techniques was evaluated using recommended indices by the World Meteorological Organization. All datasets gave a reasonable estimate of PWV when compared against GNSS at daily, monthly, and seasonal scales, the agreement between the various techniques was better at monthly and seasonal scales. In terms of bias, precision, accuracy, fitness, and reliability of measurement, ERA-Interim outperformed the other technique and could possibly be a complementary data source to GNSS PWV, although as a reanalysis, it cannot be used for meteorology. The AIRS night-time (or descending) retrieval was ranked next to the ERA-Interim; AIRS daytime (or ascending) retrieval agreed less with GNSS PWV when compared with ERA Interim and night-time AIRS. These results indicate that GNSS PWV as observed over the study area represents a remarkable dataset for further evaluation studies and serves as a useful source of humidity information to improve the water cycle in numerical weather models for varying applications in the region.     

The influence of meso-scale eddies on the current field and thermal structure of the southeastern Arabian Sea

Satellite altimetry in combination with ground-truth measurements and the Okubo–Weiss parameter-based eddy-tracking algorithm are used to study eddies in the southeastern Arabian Sea (SEAS) during the summer and winter of 2007 and 2008. In the SEAS, only the cyclonic eddy is present in summer whereas both cyclones and anticyclones are present in winter. These eddies, with dimensions of 60–120 km, propagate westward with slight north–south deflection at a speed of 5–23 cm s^?1 (mean 11.8 cm s^?1). The lifespan of eddies varies from two to six weeks. Exceptions are a cyclonic eddy in 2007 and an anticyclonic eddy in 2008 that persisted for 6 and 11 weeks, respectively. During summer and the early half of winter, wind-stress curl plays a significant role in the genesis of eddies in the SEAS. However, the propagation of these eddies is not influenced by wind-stress curl. Observations reveal that the thermal structure and currents are modified by these eddies. In winter, the signature of the cyclonic eddy is not prominent on the surface, as the water column is homogeneous up to 100 m. In the summer monsoon season, the signature of the eddy is evident up to the surface. During this period, the southward West India Coastal Current is modified locally by the cyclonic eddy formed in the SEAS.     

Sea and lake surface temperature retrieval from Landsat thermal data in Northern Germany

Landsat thermal data are employed to derive lake and sea surface temperatures. The limitations of this approach are obvious, since the calculation of surface temperatures based solely on image data requires at least two thermal bands to compensate the atmospheric influence which is mainly caused by water vapour absorption. However, the 1 km spatial resolution of currently available multi‐band thermal satellite sensors (NOAA‐AVHRR, MODIS) is often not appropriate for lake and coastal zone applications. Therefore, it is worthwhile investigating the accuracy which can be obtained with single‐band thermal data using radiosonde information of the atmospheric water vapour column from meteorological stations in the study area. In addition, standard atmospheres from the MODTRAN code were considered that are based on seasonal climatologic values of water vapour, e.g. mid‐latitude summer, mid‐latitude winter, etc.

The study area of this investigation comprises various lakes and coastal zones of the Baltic Sea in NE Germany. Landsat‐7 ETM+ imagery of nine acquisition dates was selected covering the time span from February to November 2000. Results of derived lake and sea surface temperatures were compared with in situ measurements and with an empirical model of the Deutscher Wetterdienst (Germany's National Meteorological Service, DWD). RMS deviations of 1.4 K were obtained for the satellite‐derived lake surface temperatures with respect to in situ measurements and 2.2 K with respect to the empirical DWD model. RMS deviations of 1.6 K were obtained with respect to in situ bulk temperatures in coastal zones of the Baltic Sea. This level of agreement can be considered as satisfactory given the principal constraints of this approach. A better accuracy can only be obtained with high spatial resolution (<100 m) multi‐band thermal instruments delivering imagery on an operational basis.     

Observation of atmospheric effects on repeat-pass interferometric synthetic aperture radar over the Kuwait desert

Twenty-eight advanced synthetic aperture radar (ASAR) scenes from the Environmental Satellite (ENVISAT) are analysed to select suitable pairs for generating a digital elevation model (DEM) and displacement maps. For this purpose, the repeat-pass interferometric synthetic aperture radar (InSAR) technique is implemented using GAMMA interferometric modules. The perpendicular component of baseline (B┴) is taken as the criteria for selecting the pairs: 0 < B┴ <100 m for displacement maps and 200 < B┴ < 400 m for the DEM. Though there are many pairs satisfying the above criteria, only four case studies are presented here to illustrate the effects of atmosphere on the DEM and displacement maps over the Kuwait desert climate. In each case study, two examples are selected: one where the atmosphere is a serious problem and another example the atmosphere has no significant problem. The DEM of the Shuttle Radar Topographic Mission (SRTM) is taken as a reference for root mean square (RMS) error estimation in the DEM. The RMS error varies from as low as 2 m to as high as 40 m. Some DEMs showed fringe-like structures resembling precipitable water vapour (PWV) fields. Similarly, the measured displacement values were found to vary randomly from place to place and time to time. The displacement maps showed vertical structures similar to PWV. The DEM was corrected for PWV. The results are encouraging. From this study, it is clear that, even for desert areas, there is a need to look into the effects of PWV on the DEM and displacement maps before the results are used.     

Remote sensing of Antarctic ozone depletion using GPS meteorology

The Antarctic ozone depletion has been characterized for the first time using global positioning system (GPS) meteorology through atmospheric precipitable water vapour (PWV). Based on observations conducted during 2009 at four selected stations in Antarctica, the PWV showed a few significant high peaks, and their variations agreed very well with the physical properties of the surface temperature. The relative humidity variation was relatively flat, and the pressure trend tended to decrease from August, lasting until spring, probably reflecting the warming air column and the influence of lower latitude air over the stations. Throughout the ozone depletion period, the PWV variability has been marked low compared to that of the total column ozone (TCO). This implies that more water vapour condenses in cold temperatures to form clouds that could annihilate ozone in the stratosphere. Based on a detailed comparison of PWV with TCO variation, we confirmed that the effect of GPS signals on the PWV variability during periods of ozone depletion was strongest in the troposphere compared to the stratosphere because of the polar vortex.     

High resolution spatio‐temporal water vapour mapping using GPS and MERIS observations

Improved knowledge of atmospheric water vapour and its temporal and spatial variability is of great scientific interest for climate research and weather prediction. Moreover, the availability of fine resolution water vapour maps is expected to reduce significant errors in applications using the Global Positioning System, GPS, or radar interferometry. Several methods exist to estimate water vapour using satellite systems. Combining radiances as measured in two spectral bands of the Medium Resolution Imaging Spectrometer (MERIS) results in an Integrated Water Vapor (IWV) product with high spatial resolution, up to 300 m, but a limited temporal resolution of about three days, in case of cloud free conditions. On the other hand, IWV estimates can be derived from the zenith total delays as observed by continuous GPS networks. The GPS IWV estimates have a higher temporal resolution of typically 1 hour, but, even in Western Europe, inter‐station distances are at least tenths of kilometres. Here we describe how to obtain IWV products with high spatio‐temporal resolution by combining GPS and MERIS IWV estimates. For this purpose an analysis is made of MERIS and GPS based IWV data, retrieved at the same day over Western Europe. A variance–covariance analysis is performed and is subsequently applied to produce time series of combined high‐resolution water vapour maps using Kriging. The research presented here is a first step towards near real‐time fine resolution water vapour products.     

Development of the Microwave calibrated Infrared Split‐window Technique (MIST) for rainfall estimation

An infrared rainfall estimation technique that includes information from a split window is developed. The split window refers to the difference in the brightness between the far infrared (IR) channels situated at around 10 µm and 12 µm, which has been used to estimate atmospheric water vapour and for rain area detection. The technique, called the Microwave calibrated Infrared Split‐window Technique (MIST), can be considered an extension of the Adjusted GOES Precipitation Index (AGPI). IR rain rates are first estimated from an IR‐rain rate relation derived from matching the monthly histograms of combined microwave rain estimates (3B40RT) produced by the Tropical Rainfall Measuring Mission (TRMM) Multi‐satellite Precipitation Analysis (TMPA) and the infrared data observed from a geostationary satellite. The novelty is the inclusion of the split‐window information to eliminate non‐rainy pixels as a second step. The technique has been applied to Geostationary Meteorological Satellite (GMS) and Geostationary Operational Environmental Satellite (GOES) data and tested for a dry and a wet period. The results show that the MIST has comparable biases and better rain event detection skill than the TMPA, although the TMPA is constrained by the gauge analysis by design while the MIST has no direct gauge input.     

Anthropogenic aerosol fraction over the Indian region: model simulations versus multi-satellite data analysis

Anthropogenic aerosols play a crucial role in our environment, climate, and health. Assessment of spatial and temporal variation in anthropogenic aerosols is essential to determine their impact. Aerosols are of natural and anthropogenic origin and together constitute a composite aerosol system. Information about either component needs elimination of the other from the composite aerosol system. In the present work we estimated the anthropogenic aerosol fraction (AF) over the Indian region following two different approaches and inter-compared the estimates. We espouse multi-satellite data analysis and model simulations (using the CHIMERE Chemical transport model) to derive natural aerosol distribution, which was subsequently used to estimate AF over the Indian subcontinent. These two approaches are significantly different from each other. Natural aerosol satellite-derived information was extracted in terms of optical depth while model simulations yielded mass concentration. Anthropogenic aerosol fraction distribution was studied over two periods in 2008: pre-monsoon (March–May) and winter (November–February) in regard to the known distinct seasonality in aerosol loading and type over the Indian region. Although both techniques have derived the same property, considerable differences were noted in temporal and spatial distribution. Satellite retrieval of AF showed maximum values during the pre-monsoon and summer months while lowest values were observed in winter. On the other hand, model simulations showed the highest concentration of AF in winter and the lowest during pre-monsoon and summer months. Both techniques provided an annual average AF of comparable magnitude (~0.43 ± 0.06 from the satellite and ~0.48 ± 0.19 from the model). For winter months the model-estimated AF was ~0.62 ± 0.09, significantly higher than that (0.39 ± 0.05) estimated from the satellite, while during pre-monsoon months satellite-estimated AF was ~0.46 ± 0.06 and the model simulation estimation ~0.53 ± 0.14. Preliminary results from this work indicate that model-simulated results are nearer to the actual variation as compared to satellite estimation in view of general seasonal variation in aerosol concentrations.     

FY-3气象卫星MERSI数据快速预处理的IDL实现

作为新型卫星数据源,FY\|3/MERSI(风云三号中分辨率光谱成像仪)影像的快速预处理方法与模块目前较少见。采用基于三角网的几何校正算法,根据研究区Shapefile文件和FY\|3/MERSI自带定位数据提取处理区域,实现对应区域全部20个通道几何校正,并利用后向映射重采样输出各通道图像纠正后像元点DN值,随后进行辐射定标、太阳高度角订正等预处理。该系列过程用IDL编写相关程序实现了用户界面化交互操作。运行结果表明:设计的算法流程占用内存小,计算速度快。对于一个面积约45万km²的区域,所有通道处理耗时仅450 s,且校正后影像质量和精度均较好。相较于相同区域借助ENVI软件下地理位置查找表法(GLT)仅实现单通道几何校正耗时超30 min,本文开发的模块大大提高了FY\|3/MERSI数据处理效率,为其在城市热环境、积雪等领域的应用做好准备工作。     

Simulation of satellite water vapour lidar measurements: Performance assessment under real atmospheric conditions

A lidar simulator has been applied to assess the performances of a satellite water vapour differential absorption lidar (DIAL) system. Measurements performed by the airborne Deutsches Zentrum für Luft-und Raumfahrt (DLR) water vapour DIAL on 15 May 2002 during ESA's Water Vapour Lidar Experiment (WALEX), in combination with PSU/NCAR Mesoscale Model (MM5) output, were used to obtain backscatter and water vapour fields with high resolution and accuracy. These data and model output serve as input for the simulator, allowing for the performance of satellite DIAL under highly-inhomogeneous atmospheric conditions including clouds to be assessed. The airborne measurements show an intrusion of stratospheric air into the troposphere, and MM5 data used above the DLR Falcon airplane flight altitude are characterized by very high upper tropospheric humidity levels, comparable to those associated with strong mid-latitude transport events from the troposphere to the lowermost stratosphere. Results of the simulator reveal that the maximum systematic error does not exceed 5% up to 16 km, except in the presence of thick cirrus and mid level clouds with an optical thickness up to 2 and, occasionally, inside the dry stratospheric intrusion, while the random error is less than 20% up to 16 km when spatial measurement resolutions are applied that follow the World Meteorological Organization (WMO) threshold observational requirements for numerical weather prediction (NWP). The bias is even smaller if a drier upper troposphere/lower stratosphere (UTLS) region from a reference atmosphere is considered. The results confirm the capability of satellite water vapour DIAL systems to retrieve thin structures of the tropospheric water vapour and particle backscatter fields, as well as its capability to provide low bias and random error measurements even in the presence of clouds.     

Advanced InSAR atmospheric correction: MERIS/MODIS combination and stacked water vapour models

A major source of error for repeat‐pass Interferometric Synthetic Aperture Radar (InSAR) is the phase delay in radio signal propagation through the atmosphere (especially the part due to tropospheric water vapour). Based on experience with the Global Positioning System (GPS)/Moderate Resolution Imaging Spectroradiometer (MODIS) integrated model and the Medium Resolution Imaging Spectrometer (MERIS) correction model, two new advanced InSAR water vapour correction models are demonstrated using both MERIS and MODIS data: (1) the MERIS/MODIS combination correction model (MMCC); and (2) the MERIS/MODIS stacked correction model (MMSC). The applications of both the MMCC and MMSC models to ENVISAT Advanced Synthetic Aperture Radar (ASAR) data over the Southern California Integrated GPS Network (SCIGN) region showed a significant reduction in water vapour effects on ASAR interferograms, with the root mean square (RMS) differences between GPS‐ and InSAR‐derived range changes in the line‐of‐sight (LOS) direction decreasing from ～10 mm before correction to ～5 mm after correction, which is similar to the GPS/MODIS integrated and MERIS correction models. It is expected that these two advanced water vapour correction models can expand the application of MERIS and MODIS data for InSAR atmospheric correction. A simple but effective approach has been developed to destripe Terra MODIS images contaminated by radiometric calibration errors. Another two limiting factors on the MMCC and MMSC models have also been investigated in this paper: (1) the impact of the time difference between MODIS and SAR data; and (2) the frequency of cloud‐free conditions at the global scale.