Modelling rainfall intensity from NOAA AVHRR data for operational flood forecasting in Malaysia

Many empirical studies in numerical weather prediction have been carried out that establish the relationship between top‐of‐the‐cloud brightness temperature and rainfall particularly in tropical and equatorial regions of the world. Malaysia is a tropical country that lies along the path of the north‐east and south‐west monsoon rainfall, which sometimes causes extensive flood disasters. Observations have generally shown that heavy cumulonimbus cloud formation and thunderstorms precede the usual heavy monsoon rains that cause flood disasters in the region. In this study, a model has been developed to process National Oceanic & Atmospheric Administration Advanced Very High Resolution Radiometer (AVHRR) satellite data for rainfall intensity in an attempt to improve quantitative precipitation forecasting (QPF) as input to operational hydro‐meteorological flood early warning. The thermal bands in the multispectral AVHRR data were processed for brightness temperature. Data were further processed to determine cloud height and classification performed to delineate clouds in three broad classes of low, middle, and high. A rainfall intensity of 3–12 mm h^?1 was assigned to the 1‐D cloud model to determine the maximum rain rate as a function of maximum cloud height and minimum cloud model temperature at a threshold level of 235 K. The result of establishing the rainfall intensity based on top of the cloud brightness temperature was very promising. It also showed a good areal coverage that delineated areas likely to receive intense rainfall on a regional scale. With a spatial resolution of 1.1 km, data are course but provide a good coverage for an average river catchment/basin. This raises the opportunity of simulating rainfall runoff for the river catchment through the coupling of a suitable hydro‐dynamic model and GIS to provide early warning prior to the actual rainfall event.     

Simulations of microwave brightness temperatures at AMSU-B frequencies over a 3D convective cloud system

Numerical simulations have been carried out to understand the effects of clouds associated with a tropical deep convective cloud system on the Advanced Microwave Sensor Unit-B (AMSU-B) channels at 89, 150, 183.3 ± 7, 183.3 ± 3, and 183.3 ± 1 GHz. The hydrometeor profiles including cloud liquid water, cloud ice, snow, graupel, and rain water for a deep convective cloud system simulated by a realistic dynamical cloud model, the Goddard Cumulus Ensemble model, have been input to a Vector Discrete Ordinate Radiative Transfer model to simulate the nadir down-looking microwave brightness temperatures at the top of the atmosphere. It is found that the AMSU-B channels have large brightness temperature depressions occurring over the clouds with large ice water paths. Moreover, for the three water vapour sounding frequencies around 183.3 GHz, the frequencies broader and further away from the centre of the water vapour absorption line show stronger depressions. The three water vapour channels, particularly the channels closer to the absorption line centre, essentially have negligible influence from liquid water. However, the window frequencies at 89 and 150 GHz have distinct influence from liquid water, particularly the 150 GHz, although they are also strongly influenced by frozen hydrometeors. The AMSU-B frequencies at 150 GHz and water vapour channels of 183.3 ± 7 and 183.3 ± 3 GHz are sensitive to cirrus clouds with total ice water paths above 0.1–0.2 kg m^?2. The influence of deep convective clouds and thick cirrus clouds on the AMSU-B water vapour channels demonstrates that they have a potential to estimate ice water paths in thick cirrus clouds and in the upper parts of deep convective clouds, which can complement the retrievals from the 89 and 150 GHz channels.     

Determination of the cloud fraction in the SCIAMACHY ground scene using MERIS spectral measurements

SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CartograpHY) is a passive remote sensing spectrometer observing backscattered radiation from the atmosphere and the Earth's surface, in the wavelength range between 240 and 2380 nm. The instrument is onboard ENVironmental SATellite (ENVISAT) which was launched on 1 March 2002. The Medium Resolution Imaging Spectrometer (MERIS) is also one of the 10 instruments onboard the ENVISAT satellite. MERIS is a 68.5° field-of-view nadir-pointing imaging spectrometer which measures the solar radiation reflected by the Earth in 15 spectral bands (visible and near-infrared). It obtains a global coverage of the Earth in three days. Its main objective is to measure sea colour and quantify ocean chlorophyll content and sediment, thus providing information on the ocean carbon cycle and thermal regime. It is also used to derive the cloud top height, aerosol and cloud optical thickness, and water vapour column. The ground spatial resolution of the instrument is 260 m × 290 m. This paper is aimed at determining the cloud fraction in SCIAMACHY pixels (typically, 30 km × 60 km ground scenes) using MERIS observations and number of thresholds for MERIS top-of-atmosphere reflectances and their ratios. Thresholds utilize the fact that clouds are bright white objects having similar reflectances in the blue and red. The MERIS cloud fraction has been derived for a number of SCIAMACHY states with area of 916 km × 400 km. The results are compared with correspondent cloud fractions obtained using SCIAMACHY polarization measurement devices (PMDs). Large differences are found between cloud fractions derived using SCIAMACHY and MERIS measurements. It is recommended to use highly spatially resolved MERIS observations instead of SCIAMACHY PMD measurements to retrieve cloud fractions in SCIAMACHY pixels. The improvements advised will enhance SCIAMACHY trace gas and cloud retrievals in the presence of broken cloud fields.     

Satellite‐derived cloud top pressure product validation using aircraft‐based cloud physics lidar data from the ATReC field campaign

The primary objective of this study was to assess the accuracy of satellite‐derived estimates of cloud‐top height (CTH). These estimates were derived using hourly data from the Geostationary Operational Environmental Satellite (GOES‐12) Imager and Sounder instruments. In addition, CTHs were derived using data from the MODerate resolution Imaging Spectrometer (MODIS), located on the polar‐orbiting Aqua platform. Cloud physics lidar (CPL) data taken during the Atlantic‐THORPEX Regional Campaign (ATReC) were used as the reference data set. Two cases were examined, one containing clouds at many different levels (5 December 2003) and one consisting entirely of mid‐level clouds (between 4 and 10 km, 28 November 2003). For the first case, 19.4% of the Sounder pixels and 28.0% of the Imager pixels were within ±0.5 km of the CPL measurement, while 51.5% of the Sounder pixels and 64.3% of the Imager pixels were within ±1.5 km. For the second case, 29.7% of the Sounder pixels and 39.9% of the Imager pixels were within ±0.5 km of the CPL measurement, while 85.2% of the Sounder pixels and 85.1% of the Imager pixels were within ±1.5 km. The results indicate that MODIS CTH retrievals may provide an improvement over heights derived using geostationary instruments, especially for cases where cloud heights are not highly variable.     

A novel algorithm of cloud detection for water quality studies using 250 m downscaled MODIS imagery

ABSTRACT

This study is part of a project aimed at developing an automated algorithm for algal bloom detection and quantification in inland water bodies using Moderate Resolution Imaging Spectroradiometer (MODIS) imagery. An important step is to adequately detect and exclude clouds and haze because their presence affects chlorophyll-a (chl-a) estimations. Currently available cloud masking products appear to be ineffective in turbid coastal waters. The purpose of this study is to develop a cloud masking algorithm based on a probabilistic algorithm (Linear Discriminant Analysis) and designed for water bodies by using MODIS images downscaled at a 250 m spatial resolution (MODIS-D-250). Confusion matrix shows that the new cloud mask algorithm yields very satisfactory results, enabling water classification for heavy turbid conditions with a mean kappa coefficient (κ) of 0.993 and a 95% confidence interval ranging from 0.990 to 0.997. The model also shows a very low commission error (sensitive to the presence of haze), which is essential for accurate water quality monitoring, knowing that the presence of clouds/haze/aerosols leads to major issues in the estimation of water quality parameters. The cloud mask model applied on MODIS-D-250 images improves the sensitivity to haze and the classification of turbid waters located at the edge of urban areas better than the operational MODIS products, and it clearly shows an improvement of the spatial resolution (250 m spatial resolution) compared to other cloud mask algorithms (500 m or 1 km spatial resolution), leading to an increase in exploitable data for water quality studies.     

Impact of MODIS-derived cloud cover on aerosol optical depth measured with a sun photometer: a case study from Nanjing,China

Sun photometers have been used increasingly to monitor the atmospheric environment by measuring indicators such as aerosol optical depth (AOD). However, ground-measured AOD results are subject to the presence of clouds in the air. When cloud cover is not extensive, it is still possible to use sun photometry to determine AOD, even though accuracy is reduced by cloud contamination. This research aims to detect cloud cover from Moderate Resolution Imaging Spectroradiometer (MODIS) data and then assess its impact on in situ-measured AOD. Normalized difference cloud index (NDCI) and linear spectral unmixing (LSU) were used to detect cloud cover from MODIS data. AOD at the time of acquisition of MODIS data was measured on the ground by sun photometry within 20 min of satellite overpasses (10 min before and 10 min after). Correlation analysis of NDCI- and LSU-derived cloud cover with in situ-measured AOD data demonstrates that LSU has a higher correlation coefficient with AOD than with NDCI. At 550 nm, a unit of cloud cover (e.g. 1%) raises ground-observed AOD by 0.0157. The findings of this study can be used to modify ground-derived AOD results to improve their reliability.     

Neural network application for cloud detection in SPOT VEGETATION images

SPOT VEGETATION is a recent sensor at 1 km resolution for land surface studies. Cloud detection based on this sensor is complicated by the absence of a thermal band. An artificial neural network was thus trained for the cloud detection on atmospherically corrected S1 daily data and on top of the atmosphere reflectance P data, from the SPOT VEGETATION system. It consists of a multi‐layer perceptron with one hidden sigmoid layer, trained with the Levenberg–Marquardt back‐propagation algorithm and generalized by the Bayesian regularization. Two neural networks allowed optimal cloud detections to be obtained. The first used all four bands of S1 data with 13 hidden nodes, and the second employed all four bands of P data with 11 hidden nodes. The multiple‐layer perceptrons lead to a cloud detection accuracy of 98.0% and 97.6% for S1 and P data, respectively, when trained to map three predefined values that classify cloud, water and land. The network was further evaluated using three SPOT VEGETATION images taken at different dates. The network detected not only bright thick clouds but also thin or less bright clouds. The analysis demonstrated the superior classification of the network over the standard cloud masks provided with the data.     

Observational study of cloud base height and its frequency over a tropical station,Thiruvananthapuram, using a ceilometer

This study aims to investigate the characteristic features of cloud base height (CBH) over Thiruvananthapuram during different seasons. CBH data were used for the present work derived from the Vaisala Laser Ceilometer, CL31 (VLC) installed at the campus of the Centre for Earth Science Studies, Akkulam (8.29° N, 76.59° E, 15 m above sea level). The VLC was in operation from the second week of July 2006 onwards. From the study, we found that CBH shows distinct diurnal and seasonal variations during all the seasons (except on rainy days). The diurnal variation for low-level clouds was different from that for the mid-level clouds. A cloud-free layer is evident in the region between 2.5 and 4 km. This cloud-free zone is more prominent during the southwest monsoon period compared to other seasons. Moreover, the monthly variations of cloud frequency and CBH were also described in addition to the different periodicities in cloud frequency. The periodicities found in the cloud frequency were 8 days and 30 days and these are significant at the 5% level. Thermodynamic parameters from the radiosonde were also related to the cloud frequency for various seasons and they were in good agreement.     

Multisatellite observations and numerical simulation of an along‐coast cumulus cloud line induced by sea‐breeze circulation

A coastal cumulus cloud‐line formation along the east coast of the USA was observed on a National Oceanic and Atmospheric Administration (NOAA) Polar Orbiting Environmental Satellite (POES) Advanced Very High Resolution Radiometer (AVHRR) satellite image from 17 August 2001. The cloud line starts to form at about 16:00 UTC (local 12:00 noon) and follows the coastline from Florida to North Carolina. The length and width of the cloud line are about 850 km and 8.5 km, respectively. A 15‐min interval sequence of NOAA Geostationary Operational Environmental Satellite (GOES) images shows that the cloud line maintains the shape of the coastline and penetrates inland for more than 20 km over the next 6‐h timespan. Model simulation with actual atmospheric conditions as inputs shows that the cloud line is formed near the land–sea surface temperature (SST) gradient. The synoptic flow at all model levels is in the offshore direction prior to 16:00 UTC whereas low‐level winds (below 980 hPa) reverse direction to blow inland after 16:00 UTC. This reversal is due to the fact that local diurnal heating over the land takes place on shorter time‐scales than over the ocean. The vertical wind at these levels becomes stronger as the land–SST increases during the summer afternoon, and the leading edge of the head of the inland wind ascends from 920 hPa to about 850 hPa in the 3 h after 16:00 UTC. Model simulation and satellite observations show that the cloud line becomes very weak after 21:00 UTC when the diurnal heating decreases.     

The 5S-like formulation of the signal in the O2-A band: application to cloud detection

When clouds cannot be detected by a radiometry approach, the O₂-A absorption band, located around 762 nm, can be used under clear sky conditions to perform this task. We present in this article a 5S-like formalism for the O₂ band, based on single scattering approximation, aiming to isolate the aerosol contribution. The apparent pressure of the aerosols layer, derived from this new formalism, is used in the processing chain for the Medium Resolution Imaging Spectrometer (MERIS; on-board the ENVISAT platform) third processing to detect the presence of cirrus clouds over water. This formalism can be used as well over land to correct surface pressure, derived from the O₂-A band, from the influence of the atmosphere.     

Night-time cloud detection for FY-3A/VIRR using multispectral thresholds

Night-time cloud detection using satellite data is a challenging area of research. This article presents a night-time cloud detection algorithm based on multispectral thresholds for the Visible and Infrared Radiometer (VIRR). VIRR is one of the keystone instruments on board the Chinese Feng Yun 3 (FY-3) polar-orbiting meteorological satellite. In this algorithm, three thermal infrared channels and other ancillary data are used to test for the presence of clouds according to different underlying surface types, and the four levels of possible cloud confidence are used to report whether a pixel is cloudy or clear. This algorithm strengthens the ability of identification of low cloud using the brightness temperature difference between the 3.7 and 12 μm channels. The comparisons of a new cloud mask with the official VIRR cloud mask product and with the official Moderate Resolution Imaging Spectroradiometer (MODIS) cloud mask product are shown to illustrate and validate the effect of this new algorithm. In addition, this algorithm is applied to FY-3B/VIRR data to test the validity and accuracy of cloud detection.     

Quantifying scaling effects on satellite‐derived forest area estimates for the conterminous USA

We quantified the scaling effects on forest area estimates for the conterminous USA using regression analysis and the National Land Cover Dataset 30 m satellite‐derived maps in 2001 and 1992. The original data were aggregated to: (1) broad cover types (forest vs. non‐forest); and (2) coarser resolutions (1 km and 10 km). Standard errors of the model estimates were 2.3% and 4.9% at 1 km and 10 km resolutions, respectively. Our model improved the accuracies for 1 km by 0.6% (12 556 km²) in 2001 and 1.9% (43 198 km²) in 1992, compared to the forest estimates before the adjustments. Forest area observed from Moderate Resolution Imaging Spectroradiometer (MODIS) 2001 1 km land‐cover map for the conterminous USA might differ by 80 811 km² from what would be observed if MODIS was available at 30 m. Of this difference, 58% (46 870 km²) could be a relatively small net improvement, equivalent to 1444 Tg (or 1.5%) of total non‐soil forest CO₂ stocks. With increasing attention to accurate monitoring and evaluation of forest area changes for different regions of the globe, our results could facilitate the removal of bias from large‐scale estimates based on remote sensors with coarse resolutions.     

Estimation of cloud liquid water over oceans from dual oxygen absorption band to support the assimilation of second generation of microwave observation on board the Chinese FY-3 satellite

To make up for the absence of observation-based information of cloud liquid water (CLW) in assimilation of second generation of microwave observation on board the Chinese FengYun-3 satellite, two algorithms using double oxygen-absorption band microwave sounding observation at 52.80 and 118.75 ± 2.5 GHz, one using brightness temperatures directly and the other utilizing a cloud emission and scattering index derived from the brightness temperature, are proposed to estimate CLW over oceans. Their performance was evaluated by verifying the estimations from FY-3C double oxygen absorption band microwave observations and that from the traditional Grody scheme applied to microwave measurements at 23.8 and 31.4 GHz from the MetOp-B satellite. An additional experiment was conducted to investigate the impact of regression analysis on the actual brightness temperature and reanalysis data, or the simulated measurements. It is demonstrated that CLW can be retrieved from double oxygen absorption band microwave sounding measurements. The estimations are comparable to the results obtained using the traditional scheme applied to Advanced Microwave Sounding Unit measurements. While total precipitable water was not well obtained as the traditional scheme did, it is feasible to perform regression analysis on actual brightness temperature and reanalysis data; however, for all estimations that the regression was conducted on, the results obtained using actual brightness temperature and reanalysis data were weaker than those obtained using regression coefficients from the simulated data set. The results could be improved by better matching the satellite observations and CLW data used in the regression analysis.     

Cloud parameter retrievals from Meteosat and their effects on the shortwave radiation at the surface

A method based on Spinning Enhanced Visible and Infrared Imager (SEVIRI) measured reflectance at 0.6 and 3.9 µm is used to retrieve the cloud optical thickness (COT) and cloud effective radius (r_e) over the Iberian Peninsula. A sensitivity analysis of simulated retrievals to the input parameters demonstrates that the cloud top height is an important factor in satellite retrievals of COT and r_e with uncertainties around 10% for small values of COT and r_e; for water clouds these uncertainties can be greater than 10% for small values of r_e. The uncertainties found related with geometries are around 3%. The COT and r_e are assessed using well-known satellite cloud products, showing that the method used characterize the cloud field with more than 80% (82%) of the absolute differences between COT (r_e) mean values of all clouds (water plus ice clouds) centred in the range from ±10 (±10 µm), with absolute bias lower than 2 (2 μm) for COT (r_e) and root mean square error values lower than 10 (8 μm) for COT (r_e). The cloud water path (CWP), derived from satellite retrievals, and the shortwave cloud radiative effect at the surface (CRE_SW) are related for high fractional sky covers (F_sc >0.8), showing that water clouds produce more negative CRE_SW than ice clouds. The COT retrieved was also related to the cloud modification factor, which exhibits reductions and enhancements of the surface SW radiation of the order of 80% and 30%, respectively, for COT values lower than 10. A selected case study shows, using a ground-based sky camera that some situations classified by the satellite with high F_sc values correspond to situations of broken clouds where the enhancements actually occur. For this case study, a closure between the liquid water path (LWP) obtained from the satellite retrievals and the same cloud quantity obtained from ground-based microwave measurements was performed showing a good agreement between both LWP data set values.     

Simulation of microphysical structure associated with tropical cloud clusters using mesoscale model and comparison with TRMM observations

An attempt has been made in the present study to examine the microphysical structure of a non‐squall Tropical Cloud Cluster (TCC). Three‐dimensional model simulations of cloud microphysical structure associated with a non‐squall TCC occurred on 26 October 2005 over the South Bay of Bengal have been carried out. The initial conditions for the model simulations were improved by incorporating upper air radiosonde observations and Indian Mesosphere Stratosphere Troposphere (MST) radar wind observations through analysis nudging. The horizontal and vertical distribution of the cloud hydrometeor fields observed from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) are compared to those simulated by a mesoscale model using a sophisticated microphysical scheme. Substantial differences are noticed in the amounts of cloud microphysical parameters, with simulated values of hydrometeors being higher than TMI retrievals. Spatial distribution of Cloud Liquid Water (CLW) and Rain Water (RNW) from TMI and model simulations correspond well with each other. The cloud microphysical structure during the initial and mature phases of the storm is also investigated. Comparisons of horizontal and vertical reflectivity structure from the TRMM‐Precipitation Radar (PR) and those simulated by the model show reflectivity cores of values greater than 30 dBZ. The TRMM‐PR echo tops are 3–4 km higher than the simulated echo tops. The 24 hr accumulated precipitation from model simulations are then verified with the combined rainfall product from the TRMM observations.     

Three-dimensional distribution of cloud types over the USA and surrounding areas observed by CloudSat

The vertical and horizontal distributions of the cloud types across different seasons and over the contiguous USA and surrounding areas are studied. The study is performed by collecting two years (2007 and 2008) of data from the CloudSat 2B-CLDCLASS product that uses effective radar reflectivity factor Ze, the presence of precipitation and ancillary data such as surface topography and the model-predicted temperature profile to classify clouds into seven distinct types. Considerable seasonal variations of the horizontal distribution of the cloud-type fractions are observed in the study area among different seasons and for both daytime and night-time CloudSat observations. It was found that during spring and summer, deep convective (Dc) clouds are observed much more frequently during night‐time than during daytime over both the land and ocean. For the studied area and during daytime, low clouds were more frequent (up to ?50%) over the land and less frequent over the ocean compared with night-time observations. Analysis of the vertical distribution of cloud layers reveals that the fraction of cloudy scenes with two or more distinct cloud layers is the highest (up to 30%) over the northwest corner of the USA and the southwest corner of Canada and the nearby oceans. The southwest corner of the USA and the nearby east Pacific Ocean appeared to have the lowest fraction (<0.05%) of cloudy scenes with two or more distinct cloud layers. Over the land, approximately 18% of the total cloudy scenes are classified as two-layer clouds, whereas over the ocean, two-layer clouds are less frequent and range from 13% to 17% with a stronger seasonal dependency. Only about 2–3% of the total cloudy scenes are classified as multilayer clouds, with three or more distinct layers over both the land and ocean. The vertical distribution of cloud-top heights over both the land and ocean shows two distinct peaks. Over the land, the lower peak, at around 2 km, is almost independent of season, whereas the higher peak is seasonally dependent and varies between ?8 km (during winter) and ?11 km (during summer). Over the ocean, the lower peak is also observed near 2 km (or less), whereas the higher peak ranges approximately from 11 km (during winter) to 12 km (during summer).     

Monitoring intense dust storms over the Indian region using satellite data – a case study

Dust storms are normally considered to be natural hazards. During such events, dust aerosol is loaded into the atmosphere, directly reducing visibility and effectively reflecting solar radiation back to space. In the present study, an intense dust storm was monitored during the first week of June 2010 using Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua data over the Indian region. A dust cloud was detected using a combination of MODIS reflective and emissive channels and moving trace/spread monitored by its multi-temporal data. The MODIS Terra-derived aerosol optical depth at 550 nm (AOD₅₅₀) and the aerosol index (AI) obtained from the Ozone Monitoring Instrument (OMI) were used in conjunction with National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis wind fields for the monitoring of dust clouds. The study reveals that the movement of a high concentration of dust clouds coincided with the NCEP/NCAR reanalysis meridional and zonal wind fields (>8 m s^?1) at pressure levels of 700 hPa. The Cloud–Aerosol Lidar Pathfinder Satellite Observations (CALIPSOs) that derive vertical feature mask images also suggested that the vertical extent of the dust aerosol layer was at a height of about 6 km over northern India on 2 June 2010. The roles of long-range transport of dust over the entire Gangetic plane are analysed using back trajectories from the National Oceanic and Atmospheric Administration (NOAA) Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model. Back trajectory analysis suggests that dust clouds moving over long distances entered from the western side of India on 1 June 2010.     

Merging AVHRR and SMMR data for remote sensing of ice and cloud in polar regions

Abstract

Multispectral data from the Advanced Very High Resolution Radiometer (AVHRR) were digitally processed and merged with Scanning Multichannel Microwave Radiometer (SMMR) imagery. Five channels of AVHRR data, four channels of SMMR brightness temperatures and SMMR-derived ice concentration and ice type were co-registered to a polar stereographic grid. The merged data sets are currently being used in combination with meteorological information for integrated studies of clouds and sea ice.     

A dynamic global cloud layer for virtual globes

We describe a technique to merge multiple environmental satellite data sets for an hourly updated, near real-time global depiction of cloud cover for virtual globe applications. A global thermal infrared composite obtained from merged geostationary- (GEO) and low-Earth-orbiting (LEO) satellite data is processed to depict clear and cloudy areas in a visually intuitive fashion. This GEO-plus-LEO imagery merging is complicated by the fact that each individual satellite observes a single ‘snapshot’ of the cloud patterns, each taken at different times, whereas the underlying clouds themselves are constantly moving and evolving. For the cloudy areas, the brightness and transparency are approximated based upon the cloud top temperature relative to the local radiometric surface temperatures (corrected for surface emissivity variations) at the time of the satellite observation. The technique clearly defines and represents mid- to high-level clouds over both land and ocean. Due to their proximity to the Earth's surface, low-level clouds such as stratocumulus and stratus clouds will be poorly represented with the current technique, since warmer temperatures in this case do not correspond to higher cloud transparency. Overcoming this problem requires the introduction of multispectral channel combinations.     

The influence of temperature and precipitation climate regimes on vegetation dynamics in the US Great Plains: a satellite bioclimatology case study

Advanced Very High Resolution Radiometer (AVHRR)‐derived Normalized Difference Vegetation Index (NDVI) data are widely used in global‐change research, yet relationships between the NDVI and ecoclimatological variables are not fully understood. This study attempts to model climate‐driven vegetation dynamics through the integration of satellite‐derived NDVI data with climate data collected from ground‐based meteorological stations in the US Great Plains. Monthly maximum value composites of NDVI data (8‐km resolution) and monthly temperature and precipitation records from 305 stations were collected from 1982 to 2001. Analyses involving deseasonalized datasets supported temperature as the dominant climate regime, demonstrating a higher average NDVI–temperature correlation (r = 0.73) than the NDVI–precipitation relationship (r = 0.38). Cluster analysis was used to develop a climate regionalization scheme based primarily on temperature, and NDVI characteristics of each subregion were compared. In the context of global climate change, findings from this study emphasize the influence of temperature and precipitation variability over vegetation cover in the Great Plains region.