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NASA NTRS2012

Cloud Screening and Quality Control Algorithm for Star Photometer Data: Assessment with Lidar Measurements and with All-sky Images

This paper presents the development and set up of a cloud screening and data quality control algorithm for a star photometer based on CCD camera as detector. These algorithms are necessary for passive remote sensing techniques to retrieve the columnar aerosol optical depth, delta Ae(lambda), and precipitable water vapor content, W, at nighttime. This cloud screening procedure consists of calculating moving averages of delta Ae() and W under different time-windows combined with a procedure for detecting outliers. Additionally, to avoid undesirable Ae(lambda) and W fluctuations caused by the atmospheric turbulence, the data are averaged on 30 min. The algorithm is applied to the star photometer deployed in the city of Granada (37.16 N, 3.60 W, 680 ma.s.l.; South-East of Spain) for the measurements acquired between March 2007 and September 2009. The algorithm is evaluated with correlative measurements registered by a lidar system and also with all-sky images obtained at the sunset and sunrise of the previous and following days. Promising results are obtained detecting cloud-affected data. Additionally, the cloud screening algorithm has been evaluated under different aerosol conditions including Saharan dust intrusion, biomass burning and pollution events.

data quality control algorithm↗

NASA NTRS2015

Resolving Ice Cloud Optical Thickness Biases Between CALIOP and MODIS Using Infrared Retrievals

Despite its importance as one of the key radiative properties that determines the impact of upper tropospheric clouds on the radiation balance, ice cloud optical thickness (IOT) has proven to be one of the more challenging properties to retrieve from space-based remote sensing measurements. In particular, optically thin upper tropospheric ice clouds (cirrus) have been especially challenging due to their tenuous nature, extensive spatial scales, and complex particle shapes and light scattering characteristics. The lack of independent validation motivates the investigation presented in this paper, wherein systematic biases between MODIS Collection 5 (C5) and CALIOP Version 3 (V3) unconstrained retrievals of tenuous IOT (< 3) are examined using a month of collocated A-Train observations. An initial comparison revealed a factor of two bias between the MODIS and CALIOP IOT retrievals. This bias is investigated using an infrared (IR) radiative closure approach that compares both products with MODIS IR cirrus retrievals developed for this assessment. The analysis finds that both the MODIS C5 and the unconstrained CALIOP V3 retrievals are biased (high and low, respectively) relative to the IR IOT retrievals. Based on this finding, the MODIS and CALIOP algorithms are investigated with the goal of explaining and minimizing the biases relative to the IR. For MODIS we find that the assumed ice single scattering properties used for the C5 retrievals are not consistent with the mean IR COT distribution. The C5 ice scattering database results in the asymmetry parameter (g) varying as a function of effective radius with mean values that are too large. The MODIS retrievals have been brought into agreement with the IR by adopting a new ice scattering model for Collection 6 (C6) consisting of a modified gamma distribution comprised of a single habit (severely roughened aggregated columns); the C6 ice cloud optical property models have a constant g approx. = 0.75 in the mid-visible spectrum, 5-15% smaller than C5. For CALIOP, the assumed lidar ratio for unconstrained retrievals is fixed at 25 sr for the V3 data products.This value is found to be inconsistent with the constrained (predominantly nighttime) CALIOP retrievals. An experimental data set was produced using a modified lidar ratio of 32 sr for the unconstrained retrievals (an increase of 28%), selected to provide consistency with the constrained V3 results. These modifications greatly improve the agreement with the IR and provide consistency between the MODIS and CALIOP products. Based on these results the recently released MODIS C6 optical products use the single habit distribution given above, while the upcoming CALIOP V4 unconstrained algorithm will use higher lidar ratios for unconstrained retrievals.

Cloud↗

NASA NTRS2016

New Satellite Project Aerosol-UA: Remote Sensing of Aerosols in the Terrestrial Atmosphere

We discuss the development of the Ukrainian space project Aerosol-UA which has the following three main objectives: (1) to monitor the spatial distribution of key characteristics of terrestrial tropospheric and stratospheric aerosols; (2) to provide a comprehensive observational database enabling accurate quantitative estimates of the aerosol contribution to the energy budget of the climate system; and (3) quantify the contribution of anthropogenic aerosols to climate and ecological processes. The remote sensing concept of the project is based on precise orbital measurements of the intensity and polarization of sunlight scattered by the atmosphere and the surface with a scanning polarimeter accompanied by a wide-angle multispectral imager-polarimeter. Preparations have already been made for the development of the instrument suite for the Aerosol-UA project, in particular, of the multi-channel scanning polarimeter (ScanPol) designed for remote sensing studies of the global distribution of aerosol and cloud properties (such as particle size, morphology, and composition) in the terrestrial atmosphere by polarimetric and spectrophotometric measurements of the scattered sunlight in a wide range of wavelengths and viewing directions from which a scene location is observed. ScanPol is accompanied by multispectral wide-angle imager-polarimeter (MSIP) that serves to collect information on cloud conditions and Earths surface image. Various components of the polarimeter ScanPol have been prototyped, including the opto-mechanical and electronic assemblies and the scanning mirror controller. Preliminary synthetic data simulations for the retrieval of aerosol parameters over land surfaces have been performed using the Generalized Retrieval of Aerosol and Surface Properties (GRASP) algorithm. Methods for the validation of satellite data using ground-based observations of aerosol properties are also discussed. We assume that designing, building, and launching into orbit a multi-functional high-precision scanning polarimeter and an imager-polarimeter should make a significant contribution to the study of natural and anthropogenic aerosols and their climatic and ecological effects.

remote sensing↗

NASA NTRS2001

Retrievals of Aerosol and Cloud Particle Microphysics Using Polarization and Depolarization Techniques

The recent availability of theoretical techniques for computing single and multiple scattering of light by realistic polydispersions of spherical and nonspherical particles and the strong dependence of the Stokes scattering matrix on particle size, shape, and refractive index make polarization and depolarization measurements a powerful particle characterization tool. In this presentation I will describe recent applications of photopolarimetric and lidar depolarization measurements to remote sensing characterization of tropospheric aerosols, polar stratospheric clouds (PSCs), and contrails. The talk will include (1) a short theoretical overview of the effects of particle microphysics on particle single-scattering characteristics; (2) the use of multi-angle multi-spectral photopolarimetry to retrieve the optical thickness, size distribution, refractive index, and number concentration of tropospheric aerosols over the ocean surface; and (3) the application of the T-matrix method to constraining the PSC and contrail particle microphysics using multi-spectral measurements of lidar backscatter and depolarization.

Mishchenko, Michael↗

NASA NTRS2005

Support for the Harvard University Water Vapor and Total Water Instruments for the 2004 NASA WB57 Middle Latitude Cirrus Experiment

In order to improve our understanding of the role clouds play in the climate system, NASA is investing considerable effort in characterizing clouds with instruments ranging from passive remote sensors on board the EOS platforms, to the forthcoming active remote sensors on Cloudsat and Calipso. These missions, when taken together, have the capacity to advance our understanding of the coupling between various components of the hydrologic cycle and the atmospheric circulation, and hold the additional potential of leading to significant improvements in the characterization of cloud feedbacks in global models. This is especially true considering that several of these platforms will be flown in an identical orbit within several minutes of one another-a constellation of satellites known as the A-Train. The algorithms that are being implemented and developed to convert these new data streams from radiance and reflectivity measurements into geophysical parameters invariably rely on some set of simplifymg assumptions and empirical constants. Uncertainties in these relationships lead to poorly understood random and systematic errors in the retrieved properties. This lack of understanding introduces ambiguity in interpreting the data and in using the global data sets for their intended purposes. In light of this, a series of flights with the W57F was proposed to address certain specific issues related to the basic properties of mid latitude cirrus clouds: the NASA WE357 Middle Latitude Cirrus Experiment ("MidCiX"). The science questions addressed are: 1) Can cloud property retrieval algorithms developed for A-Train active and passive remote sensing measurements accurately characterize the microphysical properties of synoptic and convectively generated cirrus cloud systems? 2) What are the relationships between the cirrus particle mass, projected area, and particle size spectrum in various genre of cirrus clouds? 3) Does the present compliment of state of the art in situ cloud probes provide the level of precision and accuracy needed to develop and validate algorithms and to contribute to our understanding of the characteristics and microphysical processes operating in cirrus clouds?

Anderson, James G.↗

NASA NTRS1999

Climatic Implications of the Observed Temperature Dependence of the Liquid Water Path of Low Clouds

The uncertainty in the global climate sensitivity to an equilibrium doubling of carbon dioxide is often stated to be 1.5-4.5 K, largely due to uncertainties in cloud feedbacks. The lower end of this range is based on the assumption or prediction in some GCMs that cloud liquid water behaves adiabatically, thus implying that cloud optical thickness will increase in a warming climate if the physical thickness of clouds is invariant. Satellite observations of low-level cloud optical thickness and liquid water path have challenged this assumption, however, at low and middle latitudes. We attempt to explain the satellite results using four years of surface remote sensing data from the Atmospheric Radiation Measurements (ARM) Cloud And Radiation Testbed (CART) site in the Southern Great Plains. We find that low cloud liquid water path is insensitive to temperature in winter but strongly decreases with temperature in summer. The latter occurs because surface relative humidity decreases with warming, causing cloud base to rise and clouds to geometrically thin. Meanwhile, inferred liquid water contents hardly vary with temperature, suggesting entrainment depletion. Physically, the temperature dependence appears to represent a transition from higher probabilities of stratified boundary layers at cold temperatures to a higher incidence of convective boundary layers at warm temperatures. The combination of our results and the earlier satellite findings imply that the minimum climate sensitivity should be revised upward from 1.5 K.

DelGenio, Anthony↗

NASA NTRS1990

Cirrus radiative characteristics and the radiative impact of small particles

An understanding of the way radiation interacts with clouds is vital for understanding the sensitivity of the earth's climate to both natural and anthropogenic changes in the atmosphere. Cirrus clouds are thought to be an important modulator of climate sensitivity. The feedback effect of cirrus on climate can be positive or negative depending upon the microphysics and scattering properties of the cloud. These properties of cirrus clouds are not well understood partly because of their thin tenuous nature and partly because of their microphysical properties. The high altitude and cold temperatures within these clouds along with their transparency greatly increase the difficulty in which accurate measurements can be obtained and interpreted both by aircraft and remote sensing. Therefore, the understanding of the interaction of radiation in cirrus clouds is crucial to determining the ways in which these clouds interact with climate forcings. The sensitivity of the radiative budgets of cirrus cloudiness to their microphysical composition and the environments in which they occur is examined. Especially important is the impact of small particles on the radiative properties of cirrus. Remote sensing estimates of the effective crystal size of cirrus and in situ measurements show large differences up to 100 microns. Thus it becomes important to identify the sources of these differences. For this reason, simulations of actual FIRE cases are compared with the in situ radiative observations and inferences are made concerning the cause of the discrepancies.

Stackhouse, Paul W., Jr.↗

NASA NTRS2016

Aircraft-Measured Indirect Cloud Effects from Biomass Burning Smoke in the Arctic and Subarctic

The incidence of wildfires in the Arctic and subarctic is increasing; in boreal North America, for example, the burned area is expected to increase by 200-300% over the next 50-100 years, which previous studies suggest could have a large effect on cloud microphysics, lifetime, albedo, and precipitation. However, the interactions between smoke particles and clouds remain poorly quantified due to confounding meteorological influences and remote sensing limitations. Here, we use data from several aircraft campaigns in the Arctic and subarctic to explore cloud microphysics in liquid-phase clouds influenced by biomass burning. Median cloud droplet radii in smoky clouds were approx. 40- 60% smaller than in background clouds. Based on the relationship between cloud droplet number (N(liq)/ and various biomass burning tracers (BBt/ across the multi-campaign data set, we calculated the magnitude of subarctic and Arctic smoke aerosol-cloud interactions (ACIs, where ACI = (1/3) x dln(N(liq))/dln(BBt)) to be approx. 0.16 out of a maximum possible value of 0.33 that would be obtained if all aerosols were to nucleate cloud droplets. Interestingly, in a separate subarctic case study with low liquid water content (0.02 gm/cu m and very high aerosol concentrations (2000- 3000/ cu cm in the most polluted clouds, the estimated ACI value was only 0.05. In this case, competition for water vapor by the high concentration of cloud condensation nuclei (CCN) strongly limited the formation of droplets and reduced the cloud albedo effect, which highlights the importance of cloud feedbacks across scales. Using our calculated ACI values, we estimate that the smoke-driven cloud albedo effect may decrease local summertime short-wave radiative flux by between 2 and 4 W/sq m or more under some low and homogeneous cloud cover conditions in the subarctic, although the changes should be smaller in high surface albedo regions of the Arctic.We lastly explore evidence suggesting that numerous northern-latitude background Aitken particles can interact with combustion particles, perhaps impacting their properties as cloud condensation and ice nuclei.

cloud microphysics↗

NASA NTRS2016

Aircraft-Measured Indirect Cloud Effects from Biomass Burning Smoke in the Arctic and Subarctic

The incidence of wildfires in the Arctic and subarctic is increasing; in boreal North America, for example, the burned area is expected to increase by 200-300 over the next 50-100 years, which previous studies suggest could have a large effect on cloud microphysics, lifetime, albedo, and precipitation. However, the interactions between smoke particles and clouds remain poorly quantified due to confounding meteorological influences and remote sensing limitations. Here, we use data from several aircraft campaigns in the Arctic and subarctic to explore cloud microphysics in liquid-phase clouds influenced by biomass burning. Median cloud droplet radii in smoky clouds were 50 smaller than in background clouds. Based on the relationship between cloud droplet number (N(liq))/ and various biomass burning tracers (BBt/ across the multi-campaign dataset, we calculated the magnitude of subarctic and Arctic smoke aerosol-cloud interactions (ACI, where ACI = (1/3) x dln(N(liq))/dln(BBt)) to be 0.12 out of a maximum possible value of 0.33 that would be obtained if all aerosols were to nucleate cloud droplets. Interestingly, in a separate subarctic case study with low liquid water content (0.02 gm/ cu m) and very high aerosol concentrations (2000-3000 cu m) in the most polluted clouds, the estimated ACI value was only 0.06. In this case, competition for water vapor by the high concentration of CCN strongly limited the formation of droplets and reduced the cloud albedo effect, which highlights the importance of cloud feedbacks across scales. Using our calculated ACI values, we estimate that the smoke-driven cloud albedo effect may decrease shortwave radiative flux by 2 and 4 W/sq or more under some low and homogeneous cloud cover conditions in the subarctic, although the changes should be smaller in high surface albedo regions of the Arctic. We lastly show evidence to suggest that numerous northern latitude background Aitken particles can interact with combustion particles, perhaps impacting their properties as cloud condensation and ice nuclei. However, the influence of background particles on smoke-driven indirect effects is currently unclear.

Arctic↗

NASA NTRS1982

Computational modeling of multispectral remote sensing systems: Background investigations

A computational model of the deterministic and stochastic process of remote sensing has been developed based upon the results of the investigations presented. The model is used in studying concepts for improving worldwide environment and resource monitoring. A review of various atmospheric radiative transfer models is presented as well as details of the selected model. Functional forms for spectral diffuse reflectance with variability introduced are also presented. A cloud detection algorithm and the stochastic nature of remote sensing data with its implications are considered.

Aherron, R. M.↗

NASA NTRS2016

Validation of Cloud Parameters Derived from Geostationary Satellites, AVHRR, MODIS, and VIIRS Using SatCORPS Algorithms

Validation is a key component of remote sensing that can take many different forms. The NASA LaRC Satellite ClOud and Radiative Property retrieval System (SatCORPS) is applied to many different imager datasets including those from the geostationary satellites, Meteosat, Himiwari-8, INSAT-3D, GOES, and MTSAT, as well as from the low-Earth orbiting satellite imagers, MODIS, AVHRR, and VIIRS. While each of these imagers have similar sets of channels with wavelengths near 0.65, 3.7, 11, and 12 micrometers, many differences among them can lead to discrepancies in the retrievals. These differences include spatial resolution, spectral response functions, viewing conditions, and calibrations, among others. Even when analyzed with nearly identical algorithms, it is necessary, because of those discrepancies, to validate the results from each imager separately in order to assess the uncertainties in the individual parameters. This paper presents comparisons of various SatCORPS-retrieved cloud parameters with independent measurements and retrievals from a variety of instruments. These include surface and space-based lidar and radar data from CALIPSO and CloudSat, respectively, to assess the cloud fraction, height, base, optical depth, and ice water path; satellite and surface microwave radiometers to evaluate cloud liquid water path; surface-based radiometers to evaluate optical depth and effective particle size; and airborne in-situ data to evaluate ice water content, effective particle size, and other parameters. The results of comparisons are compared and contrasted and the factors influencing the differences are discussed.

Minnis, P.↗

NASA NTRS2008

Absorption in Extended Inhomogeneous Clouds

The launch of several different sensors, including CloudSat, into the A-train constellation of satellites allows us for the first time to compute absorption that can occur in realistic vertically inhomogeneous clouds including multiple cloud decks. CloudSat data show that these situations are common. Therefore, understanding vertically inhomogeneous clouds is important from both climate and satellite atmospheric composition remote sensing perspectives. Satellite passive sensors that operate from the near IR to the UV often rely on radiative cloud pressures derived from absorption in oxygen bands (A, B, gamma, or O2-O2 bands) or from rotational-Raman scattering in order to retrieve information about atmospheric trace gases. The radiative cloud pressure is distinct from the physical cloud top derived from thermal infrared measurements. Therefore, the combination of information from different passive sensors yields some information about the cloud vertical profile. When either or both the clouds or atmospheric absorbers (trace gases and aerosols) are vertically inhomogeneous, the use of an effective cloud pressure derived from these approaches may lead to errors. Here, we focus on several scenarios (deep convective clouds and distinct two layer clouds) based on realistic cloud optical depth vertical profiles derived from the CloudSatfMODIS combination. We focus on implications for trace-gas column amount retrievals (specifically ozone and NO2) and derived surface UV irradiance from the Ozone Monitoring Instrument (OMI) on the Atrain Aura platform.

Joiner, Joanna↗

DOE OSTI2023

Arctic Cloud‐Base Ice Precipitation Properties Retrieved Using Bayesian Inference

Abstract Cloud‐climate feedbacks are still the greatest source of uncertainty in current climate projections. Arctic clouds, which are predominantly stratiform and supercooled, often long‐lived, and nearly continuously precipitate ice particles, contribute roughly 10% of the uncertainty attributed to the global cloud feedback. This Arctic cloud uncertainty is driven by incomplete observational and theoretical knowledge required to estimate and explain the state and active processes occurring in those clouds. A focus on ice precipitation properties at Arctic cloud base rather than the surface deconfounds the product of cloud condensate sink processes from the influence of the atmospheric thermodynamic state below cloud base, rendering cloud‐base properties a more appealing target for inference and evaluation of model simulations. Here I describe an inverse model for the estimation of cloud base ice precipitation properties over Utqiagvik, North Slope of Alaska, using the synthesis of ground‐based radar and lidar measurements. By leveraging a Markov Chain Monte Carlo algorithm as the core of the inverse model, a wide range of particle size distributions are sampled, and different combinations of ice habit models are examined, both of which are typically fixed in other retrieval methods. Results show intriguing links between different cloud base thermodynamic and ice precipitation properties. Apparent ice number concentration enhancements at temperatures of −5 and −15°C suggest possible secondary ice production (SIP). The analysis alludes to an overestimation of SIP occurrence and intensity, especially in studies relying only on radar or lidar measurements. Finally, reflectivity‐dependent ice precipitation rate and ice water content parameterizations are presented.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2017

Global Clear-Sky Surface Skin Temperature from Multiple Satellites Using a Single-Channel Algorithm with Angular Anisotropy Corrections

Surface skin temperature (T(sub s)) is an important parameter for characterizing the energy exchange at the ground/water-atmosphere interface. The Satellite ClOud and Radiation Property retrieval System (SatCORPS) employs a single-channel thermal-infrared (TIR) method to retrieve T(sub s) over clear-sky land and ocean surfaces from data taken by geostationary Earth orbit (GEO) and low Earth orbit (LEO) satellite imagers. GEO satellites can provide somewhat continuous estimates of T(sub s) over the diurnal cycle in non-polar regions, while polar T(sub s) retrievals from LEO imagers, such as the Advanced Very High Resolution Radiometer (AVHRR), can complement the GEO measurements. The combined global coverage of remotely sensed T(sub s), along with accompanying cloud and surface radiation parameters, produced in near-realtime and from historical satellite data, should be beneficial for both weather and climate applications. For example, near-realtime hourly T(sub s) observations can be assimilated in high-temporal-resolution numerical weather prediction models and historical observations can be used for validation or assimilation of climate models. Key drawbacks to the utility of TIR-derived T(sub s) data include the limitation to clear-sky conditions, the reliance on a particular set of analyses/reanalyses necessary for atmospheric corrections, and the dependence on viewing and illumination angles. Therefore, T(sub s) validation with established references is essential, as is proper evaluation of T(sub s) sensitivity to atmospheric correction source. This article presents improvements on the NASA Langley GEO satellite and AVHRR TIR-based T(sub s) product that is derived using a single-channel technique. The resulting clear-sky skin temperature values are validated with surface references and independent satellite products. Furthermore, an empirically adjusted theoretical model of satellite land surface temperature (LST) angular anisotropy is tested to improve satellite LST retrievals. Application of the anisotropic correction yields reduced mean bias and improved precision of GOES-13 LST relative to independent Moderate-resolution Imaging Spectroradiometer (MYD11_L2) LST and Atmospheric Radiation Measurement Program ground station measurements. It also significantly reduces inter-satellite differences between LSTs retrieved simultaneously from two different imagers. The implementation of these universal corrections into the SatCORPS product can yield significant improvement in near-global-scale, near-realtime, satellite-based LST measurements. The immediate availability and broad coverage of these skin temperature observations should prove valuable to modelers and climate researchers looking for improved forecasts and better understanding of the global climate model.

Scarino, Benjamin R.↗

NASA NTRS1986

Determination of cloud fields from analysis of HIRS2/MSU sounding data

IR and microwave remote sensing data collected with the HIRS2 and MSU sensors on the NOAA polar-orbiting satellites were evaluated for their effectiveness as bases for determining the cloud cover and cloud physical characteristics. Techniques employed to adjust for day-night alterations in the radiance fields are described, along with computational procedures applied to compare scene pixel values with reference values for clear skies. Sample results are provided for the mean cloud coverage detected over South America and Africa June 1979, with attention given to concurrent surface pressure and cloud top pressure values.

Susskind, J.↗

NASA NTRS2012

2D Radiative Processes Near Cloud Edges

Because of the importance and complexity of dynamical, microphysical, and radiative processes taking place near cloud edges, the transition zone between clouds and cloud free air has been the subject of intense research both in the ASR program and in the wider community. One challenge in this research is that the one-dimensional (1D) radiative models widely used in both remote sensing and dynamical simulations become less accurate near cloud edges: The large horizontal gradients in particle concentrations imply that accurate radiative calculations need to consider multi-dimensional radiative interactions among areas that have widely different optical properties. This study examines the way the importance of multidimensional shortwave radiative interactions changes as we approach cloud edges. For this, the study relies on radiative simulations performed for a multiyear dataset of clouds observed over the NSA, SGP, and TWP sites. This dataset is based on Microbase cloud profiles as well as wind measurements and ARM cloud classification products. The study analyzes the way the difference between 1D and 2D simulation results increases near cloud edges. It considers both monochromatic radiances and broadband radiative heating, and it also examines the influence of factors such as cloud type and height, and solar elevation. The results provide insights into the workings of radiative processes and may help better interpret radiance measurements and better estimate the radiative impacts of this critical region.

Varnai, T.↗

DOE OSTI2021

Application of remote sensing precipitation data and the $\mathrm{CONNECT}$ algorithm to investigate spatiotemporal variations of heavy precipitation: Case study of major floods across Iran (Spring 2019)

In recent years, the number of floods following unprecedented rainfall events have increased in Iran during early spring (March 21st to April 20th, referred to in Iran as the month of “Farvadin”). While numerous studies have addressed changes in climate extremes and precipitation trends at different temporal scales from daily to annual across the country, analyses of short-duration and heavy precipitation, especially during recent years, are rarely considered. Furthermore, most studies investigate the variations in extremes and total precipitation using a limited number of synoptic weather stations across Iran. Here this study assesses the variations in heavy precipitation (precipitation with intensities greater than or equal to 3 mm/3 h) at 0.04° spatial and 3-hourly temporal resolution during the month of Farvardin. In addition, the effect of atmospheric river conditions over Iran and their possible link to intensifying heavy precipitation is explored. For this purpose, the CONNected-objECT (CONNECT) algorithm is applied on a precipitation dataset, Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS), and an Integrated Water Vapor Transport (IVT) dataset from the NASA Modern-Era Retrospective Analysis for Research and Applications Version-2 (MERRA-2). The results suggest that the increase in the number of floods in recent years is related to the increase in the intensity and volume of heavy precipitation events, although the frequency and duration of heavy precipitation events have not changed significantly. Furthermore, the results show that atmospheric river conditions over the country are present during the same window as each year’s most extreme events. It is found that 8 out of 13 of the largest ARs over Iran come from moisture plumes with pathways over the African and Red Sea.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1985

Orbital remote sensing - Space technology applications in south-east Asia

The evolution of remote sensing techniques in the developing countries of southeast Asia is reviewed. The use of the images for monitoring soil, water, and vegetation resources, in order to develop a national policy for conservation of the resources, is described. The remote sensing data are helpful in observing deforestation in southeast Asia; however, excessive cloud coverage does not allow accurate evaluation of the rice crop. The effects of the capabilities of the developing countries to process the data and remote sensing program of industrial countries on the future application of satellite imagery in developing countries are studied. The need for improved data banking and dissemination of the imagery is analyzed. Agreements on proprietary rights due to the improved ground resolution of orbital sensors are required. The designing of remote sensing equipment to meet the requirements of its users is discussed.

Malingreau, J.-P.↗