Water Vapor Absorption Channels Research Articles

Improvements of the Microwave Gaseous Absorption Scheme Based on Statistical Regression and Its Application to ARMS

AbstractAn improved microwave gaseous absorption scheme based on statistical regression is proposed in this study. In the new scheme, Monochromatic Radiative Transfer Model (MonoRTM) replaces the Millimeter‐wave Propagation Model (MPM) to train the new scheme and the effect of real Spectral Response Functions is included in the training process. After the replacement, results of the new scheme are closer to observations from Advanced Technology Microwave Sounder (ATMS) onboard Suomi National Polar‐orbiting Partnership satellite in low level channels while MPM has some advantages in upper level channels. Introducing ozone absorption can cause a systematic bias but results in small standard deviations in channels with frequency 183 ± 1.8 GHz and 183 ± 1 GHz. In addition, the new scheme updates the vertical interpolation of water vapor and optimizes the vertical distribution of Planck function. These updates can reduce biases caused by vertical interpolation, especially for water vapor absorption channels. The bias associated to vertical interpolation can reach 0.25 K whereas the new scheme can decrease it to 0.003 K. To further validate the accuracy of the new scheme, we apply the new scheme to Advanced Radiative transfer Modeling System and compare simulated results to RTTOV 13.2 under 37 and 137 level (L) atmosphere (atm). Observations from ATMS onboard NOAA‐20 are used as true values. Results show that the new scheme agrees with RTTOV 13.2 well in accuracy and performs even better in upper level channels and water vapor absorption channels.

Arctic Winds Retrieved from FY-3D Microwave Humidity Sounder-II 183.31 GHz Brightness Temperature Using Atmospheric Motion Vector Method

In this study, we develop an Atmospheric Motion Vector (AMV)-based method for retrieving wind vectors using 183.31 GHz water-vapor absorption channels. The method involves tracking water-vapor features from image triplets and subsequently deriving wind fields from motion vectors. The height of the derived wind for each channel is determined by calculating the weighing function peak using monthly averaged ERA5 reanalysis data. By utilizing Microwave Humidity Sounder-II (MWHS-II) brightness temperatures from the five channels centered around 183.31 GHz, wind vectors are retrieved within the Arctic region for the entire year of 2022. The retrieval quality is evaluated through comparative analysis with ERA5 reanalysis data and the Visible Infrared Imaging Radiometer Suite (VIIRS) wind product. The resultant vector root mean square errors (RMSEs) are approximately 4.5 m/s for the three lower-height channels and 5.5 m/s for the two upper-height channels. These findings demonstrate a wind retrieval performance comparable to the existing methods, highlighting its potential for augmenting wind availability at lower height levels.

Rainfall Area Identification Algorithm Based on Himawari-8 Satellite Data and Analysis of its Spatiotemporal Characteristics

Real-time monitoring of rainfall areas based on satellite remote sensing is of vital importance for extreme rainfall research and disaster prediction. In this study, a new rainfall area identification algorithm was developed for the new generation of geostationary satellites with high spatial and temporal resolution and rich bands. As the main drivers of the rainfall process, the macro and micro physical properties of clouds play an important role in the formation and development of rainfall. We considered differences in the absorption capacity of the water vapor absorption channels in the infrared band and introduced a sensitivity difference of rainfall area in water vapor channels to construct a sensitive detection of the water vapor region. The results of this algorithm were evaluated using Global Precipitation Measurement (GPM) satellite products and CloudSat measurements in various scenarios, with hit rates of 70.03% and 81.39% and false alarm rates of 2.05% and 21.34%, respectively. Spatiotemporal analysis revealed that the types of upper clouds in the rainfall areas mainly consisted of deep convection, cirrostratus, and nimbostratus clouds. Our study provides supporting data for weather research and disaster prediction, as well as an efficient and reliable method for capturing temporal and spatial features.

Improving the capability of water vapor retrieval from Landsat 8 using ensemble machine learning

Precipitable water vapor (PWV) facilitates the exchange of water and energy between the Earth's surface and the surrounding atmosphere. PWV can be retrieved by employing the water vapor absorption channels in Landsat 8. Unfortunately, the conventional split-window covariance-variance ratio (SWCVR) method is susceptible to various factors, resulting in low retrieval accuracy and availability. Therefore, we improve the traditional SWCVR model and propose a new PWV retrieval method using ensemble machine learning to address these limitations. The new method uses Gradient Boosting Decision Tree (GBDT) to establish the model between brightness temperature, GNSS-derived PWV, and related surface parameters. The results of the test set show that the improved SWCVR model has an RMSE, Bias, and availability of 0.4947 g/cm2, 0.0276 g/cm2, and 29.6%, respectively. By contrast, the GBDT model's corresponding values are 0.2870 g/cm2, −0.0094 g/cm2, and 67.9%, respectively. Compared with SWCVR, the GBDT improves the RMSE and availability by 41.99% and 38.3%, respectively. The GBDT algorithm is significantly better than the SWCVR model in low altitude and complex surface coverage areas. From a temporal perspective, the advantages of the GBDT method are more apparent in the summer. Finally, the SWCVR and GBDT models are externally validated using measured PWV data from the sun photometer and radiosonde, and the RMSE is 0.5148 g/cm2 and 0.3775 g/cm2, respectively. Based on the findings, we know that the accuracy of GBDT has significantly improved against the SWCVR.

Study on the Slant-Path Effect in the Simulation of Clear-Sky Thermal Radiance for the GK2A AMI

Abstract Taking the slanted satellite viewing geometry into account is important in the simulation of satellite radiances, which vary with the atmospheric conditions along the line of sight. As a first step to take the slanted satellite viewing geometry into account in the numerical weather prediction system operated in the Korean Meteorological Administration, the slant-path modeling is applied for the simulation of clear-sky thermal radiances of a geostationary satellite imager, the Advanced Meteorological Imager (AMI) on board the Geo-KOMPSAT-2A (GK2A). The observations minus simulations (O − B) and the Jacobians before and after the slant-path calculation are compared. Since most infrared channels of AMI are not sensitive to the atmosphere above the tropopause, the size of slant-path effect for AMI is overall smaller than the effect shown in the microwave sounders. Still, the slant-path modeling is found to have a noticeable effect on the three water vapor absorption channels of AMI peaking between 300 and 600 hPa, particularly at large satellite zenith angles and on the regions with high water vapor variabilities in the model field along the line of sight. On average, the slant-path interpolation reduces the standard deviation of O − B of the water vapor channels by around 2.0% on land and 1.4% over the ocean for zenith angles 40°–60°, and it also influences not only the shape and magnitude but also the height of the peak of the Jacobians. In addition, the retrieval experiment based on the optimal estimation also demonstrates the impact of this new approach in the retrieved moisture field, though the improvement is not as significant as shown in the simulation experiment.

Angular-Dependent Polarized Characteristics of Surface Emissivity at 150 GHz Over Saharan and Taklimakan Deserts

This study characterizes the marginally-known angular-dependent polarization difference of the land surface emissivity (LSE), using the microwave humidity sounder (MWHS) onboard China’s sun-synchronous satellite FengYun-3B (FY-3B). The instrument has two quasi-polarization channels at 150 GHz in addition to the three water-vapor absorption channels in the vicinity of 183 GHz, allowing observations at quasi vertical and horizontal polarizations with its cross-track scanning geometry. The Saharan and Taklimakan deserts are taken as examples due to the relatively homogeneous surface conditions. In the two 150 GHz channels of MWHS, the polarization bias is corrected according to the measurements at the Earth incidence angle (EIA) of 0°. The monthly-mean quasi-polarization difference (QPD) at 150 GHz is found to be varying with the EIA and exhibits seasonal variations over deserts. The QPD reaches a maximum value at the EIA of ~35°, while the zero value appears near the EIA of ~53° (equal to the satellite viewing angle of 45°) as expected. In contrast to the relatively stable and low QPD over Sahara, the QPD, quasi polarized emissivity difference and pure polarized emissivity difference are generally larger in the Taklimakan Desert, where the maximum monthly QPD is ~4 K and the polarized emissivity difference increases monotonously to ~0.2 at the EIA of 50°. The complex surface parameters and temporal variabilities impinge on the uncertainties of the land polarization. The increasing soil moisture due to precipitation damps the dichroic ability of the land surface, while the frozen surface due to snowfalls in cold seasons enhances the polarization.

An Improved MODIS NIR PWV Retrieval Algorithm Based on an Artificial Neural Network Considering the Land-Cover Types

Estimating precipitable water vapor (PWV) with high accuracy and spatial resolution is important in many disciplines. Water vapor absorption and non-absorption channels can be observed in the near-infrared (NIR) ray of the Moderate Resolution Imaging Spectroradiometer (MODIS), which can be used to retrieve PWV. However, traditional algorithms overestimate the NIR PWV in North America. This study proposes a novel NIR retrieval algorithm based on machine learning that considers land-cover types to estimate high-accuracy PWV. To do this, nonlinear models between MODIS NIR transmittance, based on the 2- and 3-Channel Ratio, and global navigation satellite system (GNSS) PWV, recorded by the SuomiNet GNSS network, are established using a backpropagation neural network. Verification shows that the root mean square error (RMSE)/ standard deviation (STD)/Bias of 2-Channel Ratio PWV is 1.29/1.29/0.02 mm, and the improvements of RMSE and STD are 66.32% and 37.98%, respectively. The RMSE/STD/Bias values of the 3-Channel Ratio PWV are 1.29/1.29/0.02 mm, and the improvements in RMSE and STD are 68.67% and 42.31%, respectively. In addition, the surface verification of the proposed method in six land-cover types shows that both the 2- and 3-Channel Ratio methods can yield satisfactory PWV estimates. Compared to the MODIS PWV products, the proposed method yields remarkable progress.

Water vapor retrieval from MERSI NIR channels of Fengyun-3B satellite using ground-based GPS data

An ensemble-based empirical regression algorithm is for the first time developed to retrieve total column water vapor from the Medium Resolution Spectral Imager (MERSI) near-infrared (NIR) channels onboard the Fengyun-3B (FY-3B) satellite. This retrieval method uses precipitable water vapor (PWV) data estimated from ground-based Global Positioning System (GPS) data to build a regression model in which the reflectance ratio observed from MERSI NIR absorption channels and the corresponding GPS PWV data are the parameters. The MERSI Level 1b data, specifically the three water vapor absorption channels centered at 905 nm, 940 nm, and 980 nm are used to retrieve water vapor. PWV data observed from 256 ground-based GPS stations located in the western North America in 2016 are used as reference data for model development. Then, validation is performed with data obtained during 2017–2019 from both the western North America and Australia to assess the performance of the proposed algorithm. The results indicate that the new PWV results agree very well with ground-based PWV reference data. The mean absolute percentage error (MAPE) for ensemble median PWV is 16.72% ~ 36.74% in western North America and is 15.47% ~ 32.31% in Australia. The RMSE is 4.635 mm ~ 8.156 mm in western North America and is 5.383 mm ~ 8.900 mm in Australia. The weighted mean value using three-channel ratio transmittance has the best retrieval accuracy, with RMSE of 4.635 mm in western North America and 5.383 mm in Australia. This new PWV algorithm can retrieve PWV from FY-3B data with a higher accuracy for different regions. Different from conventional algorithms, no pre-observed information of atmospheric parameters is required in this model.

Improvement in cloud retrievals from VIIRS through the use of infrared absorption channels constructed from VIIRS+CrIS data fusion

Abstract. Retrieval of semitransparent ice cloud properties from the Visible Infrared Imaging Radiometer Suite (VIIRS) satellite sensor on the Suomi National Polar-orbiting Partnership (S-NPP) and NOAA-20 platforms is challenging due to the absence of infrared (IR) water vapor and CO2 absorption channels. However, on these platforms, there is a companion sensor called the Crosstrack Infrared Sounder (CrIS) that provides these spectral measurements but at a lower spatial resolution (∼15 km at nadir). To mitigate the lack of VIIRS spectral measurements in these IR absorption channels, recent studies suggest an approach to supplement VIIRS measurements by fusion of the imager and sounder data. In particular, Weisz et al. (2017) demonstrate a method to construct IR water vapor and CO2 absorption channel radiances for VIIRS at 750 m spatial resolution. Based on these constructed channels for both S-NPP and NOAA-20, this study evaluates three cloud properties – cloud mask, cloud thermodynamic phase, and cloud top height – through comparison to the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation/Cloud-Aerosol Lidar with Orthogonal Polarization (CALIPSO/CALIOP) V4-20 cloud layer products and Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6.1 cloud top products. Each of these cloud properties shows improvement with the use of these constructed channel radiances. The major improvement for the cloud mask is found over polar regions, where the correct cloud detection percentage increases due to a decrease in missed clouds and/or false detection. For cloud thermodynamic phase, the ice cloud fraction increases over non-polar regions and the combined liquid water and ice cloud discrimination improves in comparison with CALIPSO. The retrieved cloud top height for semitransparent ice clouds increases over non-polar regions and tends to be closer to the true CALIPSO/CALIOP cloud top height. Moreover, the uncertainty of cloud top height retrievals decreases globally for these clouds.

Water Vapor Retrieval From MODIS NIR Channels Using Ground-Based GPS Data

A novel algorithm for water vapor retrieval from Moderate Resolution Imaging Spectroradiometer (MODIS) near-infrared (NIR) channels is proposed in this research. In contrast to conventional retrieval algorithms based on radiative transfer methods, this algorithm uses the empirical regression functions to calculate precipitable water vapor (PWV). In this article, water vapor data observed from January 1, 2003, to December 31, 2017, from 464 GPS stations situated in western North America serve as reference data to determine the relationship between the transmittance of the water vapor absorption channels and atmospheric water vapor content. The model is trained on different subsets of the training data through the bootstrap resampling method. Validation results against PWV observations during the period 2010–2017 from five globally distributed GPS stations illustrate that the algorithm can significantly improve the accuracy of MODIS NIR water vapor data, with root-mean-square error (RMSE) reduction of 22.48% from 7.670 to 5.946 mm for two-channel ratio method and 21.69% from 7.670 to 6.006 mm for three-channel ratio method for MODIS/Terra satellite data, and RMSE reduction of 16.42% from 7.191 to 6.010 mm and 15.26% from 7.191 to 6.094 mm for PWV derived from two-channel and three-channel ratio methods from Aqua, respectively, for MODIS/Aqua satellite data.

Monitoring the VIIRS Sensor Data Records Reflective Solar Band Calibrations Using DCC With Collocated CrIS Measurements

AbstractAs uniform and stable objects, deep convective clouds (DCCs) are often used to monitor the calibration stabilities of the reflective solar bands (RSBs) of the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar‐orbiting Partnership. Traditionally, DCCs are identified by the legacy 11‐μm brightness temperature (BT11) threshold method. With the collocated Cross‐track Infrared Sounder (CrIS), a method of combining the BT difference between a water vapor absorption channel and a window channel to its measurement noise ratio (BNR) is adopted and applied to DCC identification. The BNR method improves the DCC detections over the legacy method because it is less contaminated with high clouds not thick and bright enough. Using observations from 2017 to 2018, the results show BNR has better performances than BT11 for identifying DCCs and monitoring RSBs. When comparing to BT11, BNR has more robust and invariant time series of monthly reflectance for all RSBs; that is, the standard deviation and total variation range (maximum‐minimum) are up to 47% smaller. Because BNR affects more on the left tails (less reflective) than the modes of the histograms of reflectance, the improvement is more significant on the mean reflectance than the mode. BNR detects fewer DCCs than BT11, but with more confidence. This allows the weekly and daily time series for monitoring RSBs in higher temporal frequencies. This method can be applied to other imagers with collocated advanced infrared sounders for detecting DCCs and monitoring the calibration stabilities of RSBs.

Multi-Channel Regression Inversion Method for Passive Remote Sensing of Ice Water Path in the Terahertz Band

Retrieval of ice cloud properties using passive terahertz wave radiometer from space has gained increasing attention currently. A multi-channel regression inversion method for passive remote sensing of ice water path (IWP) in the terahertz band is presented. The characteristics of the upward terahertz radiation in the clear-sky and cloudy-sky are first analyzed using the Atmospheric Radiative Transfer Simulator (ARTS). Nine representative center frequencies with different offsets are selected to study the changes of terahertz radiation caused by microphysical parameters of ice clouds. Then, multiple linear regression method is applied to the inversion of IWP. Combinations of different channels are selected for regression to eliminate the influence of other factors (i.e., particle size and cloud height). The optimal fitting equation are obtained by the stepwise regression method using two oxygen absorption channels (118.75 ± 1.1 GHz, 118.75 ± 3.0 GHz), two water vapor absorption channels (183.31 ± 1.0 GHz, 183.31 ± 7.0 GHz), and two window channels (243.20 ± 2.5 GHz, 874.4 ± 6.0 GHz). Finally, the errors of the proposed inversion method are evaluated. The simulation results show that the absolute errors of this method for the low IWP cases are below 7 g/m2, and the relative errors for the high IWP cases are generally ranging from 10 to 30%, indicating that the multi-channel regression inversion method can achieve satisfactory accuracy.

Remote sensing of convective clouds using multi-spectral observations and examining their variability over India

Convective clouds are sources of heavy precipitation triggering flood related disasters. They are the drivers of atmospheric circulation and fundamental part of hydrological cycle. Changes in convective clouds affect atmospheric circulation and hydrological cycle. Long term reliable record of convective clouds is very useful to understand and study the hydrological cycle. Present research focuses on remote sensing of convective clouds using multispectral measurements at thermal Near Infrared channels (near 11 and 12 µm) and water vapor absorption channels (near 6.7 µm) from Meteosat First Generation (MFG) observations. Observed convective clouds are consistent with increased ice cloud water path and integrated water vapor and decreased incoming shortwave flux over Indian monsoon region. Convective clouds obtained from the present study are validated against space based radar observations from Precipitation Radar (PR) Onboard Tropical Rainfall Measuring Mission (TRMM). It is reported that present technique shows a correlation coefficient (CC) of 0.73 and Standard error of estimate of 2.96% in detecting seasonal convective clouds over India and nearby regions. 19 years record of reliable convective clouds has been generated using multispectral observations over India and nearby regions. It is reported that convective clouds show coherent variation with regional temperature over India. Impact of regional warming on convective clouds has been investigated. Present study reports that there is an increase of about 41.56% ± 11.43% in convective clouds for a unit degree increase in regional temperature over India. Results reported in this study highlight the importance of mitigation and adaptation actions against flood like disasters caused by increased convective clouds over Indian region.

Detection flying aircraft from Landsat 8 OLI data

Monitoring flying aircraft from satellite data is important for evaluating the climate impact caused by the global aviation industry. However, due to the small size of aircraft and the complex surface types, it is almost impossible to identify aircraft from satellite data with moderate resolution, e.g. 30 m. In this study, the 1.38 μm water vapor absorption channel, often used for cirrus cloud or ash detection, is for the first time used to monitor flying aircraft from Landsat 8 data. The basic theory behind the detection of flying aircraft is that in the 1.38 μm channel most of the background reflectance between the ground and the aircraft is masked due to the strong water vapor absorption, while the signal of the flying aircraft will be attenuated less due to the low water vapor content between the satellite and the aircraft. A new composition of the Laplacian and Sobel operators for segmenting aircraft and other features were used to identify the flying aircraft. The Landsat 8 Operational Land Imager (OLI) 2.1 μm channel was used to make the method succeed under low vapor content. The accuracy assessment based on 65 Landsat 8 images indicated that the percentage of leakage is 3.18% and the percentage of false alarm is 0.532%.

Effects of Ice Decontamination on GOES-12 Imager Calibration

More precise and accurate geostationary measurements are highly needed for satellite applications. It was well known that the Geostationary Operational Environmental Satellite (GOES)-12 imager was susceptible to water-ice contamination, and thus, several decontamination efforts were carried out to remove built-up ice on the instrument during operation. The intercalibration results of GOES-12 with the Atmospheric Infrared (IR) Sounder (AIRS) and the Infrared Atmospheric Sounding Interferometer (IASI) indicate that the calibration accuracy of GOES-12 was impacted by the decontamination procedures. Relative to the AIRS and the IASI, the GOES-12 imager radiances or brightness temperatures increased in the CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> sounding channel (channel 6, 13.3 μm) and decreased in the water-vapor absorption channel (channel 3, 6.5 μm) but was less changed in the window channel (channel 4, 10.7 μm). A simple conceptual model is then proposed to give a physical explanation on the different behaviors of three IR channels in response to the ice-removal procedures.

Radiometric and spectral characterization and comparison of the Antarctic Dome C and Sonoran Desert sites for the calibration and validation of visible and near-infrared radiometers

Global climate change studies require long-term, radiometrically accurate, and stable observations from a number of satellites. Spatially uniform and radiometrically/spectrally stable vicarious calibration sites can be used to quantify the sensor gain change over time, monitor the instrument performance, and compare measurements from multiple instruments to maintain consistent radiometric calibration. This study uses AQUA moderate resolution imaging spectroradiometer (MODIS), MetOp-A advanced very high resolution radiometer (AVHRR), and Earth Observing-1 Hyperion to analyze the radiometric and spectral characteristics of the Dome C and Sonoran Desert sites and perform intercomparisons. The radiometric stability of both sites over a period of eight years as evaluated using AQUA MODIS is found to be better than 2% in the visible (0.64 μm) and near-infrared regions (0.86 μm), assuming the MODIS calibration is stable. The bidirectional reflectance distribution (BRDF) effect over Dome C is large, with greater than 5% seasonal variation, compared to the Sonoran Desert with less than 2% variation. However, the BRDF impact can be reduced to less than 2% for Dome C after normalization by an appropriate BRDF model. For water vapor absorption channels, such as AVHRR channel 2 (0.86 μm), this study suggests that the Sonoran Desert is largely affected with greater than 6% absorption variability compared to that of Dome C with less than 2%. The study also reveals that the operationally calibrated AVHRR top-of-atmosphere reflectance is lower than that of MODIS by about 8%.

A multi-sensor upper tropospheric ozone product (MUTOP) based on TES Ozone and GOES water vapor: derivation

Abstract. The Tropospheric Emission Spectrometer (TES), a hyperspectral infrared instrument on the Aura satellite, retrieves a vertical profile of tropospheric ozone. However, polar-orbiting instruments like TES provide limited nadir-view coverage. This work illustrates the value of these observations when taken in context with geostationary imagery describing synoptic-scale weather patterns. The goal of this study is to create map-view products of upper troposphere (UT) ozone through the integration of TES ozone measurements with two synoptic dynamic tracers of stratospheric influence: specific humidity derived from the GOES Imager water vapor absorption channel, and potential vorticity (PV) from an operational forecast model. As a mixing zone between tropospheric and stratospheric reservoirs, the upper troposphere (UT) exhibits a complex chemical makeup. Determination of ozone mixing ratios in this layer is especially difficult without direct in situ measurement. However, it is well understood that UT ozone is correlated with dynamical tracers like low specific humidity and high potential vorticity. Blending the advantages of two remotely sensed quantities (GOES water vapor and TES ozone) is at the core of the Multi-sensor Upper Tropospheric Ozone Product (MUTOP). Our results suggest that 72 % of TES-observed UT ozone variability can be explained by its correlation with dry air and high PV. MUTOP reproduces TES retrievals across the GOES-West domain with a root mean square error (RMSE) of 18 ppbv (part per billion by volume). There are several advantages to this multi-sensor derived product approach: (1) it is calculated from two operational fields (GOES specific humidity and GFS PV), so maps of layer-average ozone can be created and used in near real-time; (2) the product provides the spatial resolution and coverage of a geostationary image as it depicts the variable distribution of ozone in the UT; and (3) the 6 h temporal resolution of the derived product imagery allows for the visualization of rapid movement of this dynamically-driven ozone, as illustrated in the animation Supplement. This paper presents the scientific basis and methodology behind the creation of this unique ozone product, as well as a statistical comparison of the derived product to an evaluation dataset of coincident TES observations.

Utilization of the AMSU high frequency measurements for improved coastal rain retrievals

This study describes a new rain estimation technique using Advanced Microwave Sounding Unit (AMSU) high frequency measurements and applicable over coastal regions. The existing operational AMSU rain retrieval algorithm utilizes the microwave window frequency measurements at 23, 31, 89 and 150 GHz. These measurements have limited applicability over coast due to ocean surface emissivity effects. This situation often leads to missing rain extent and thus rate retrievals over coastal regions. The new technique utilizes the higher window (89 and 150 GHz), opaque oxygen (53.6 GHz) and water vapor absorption (183 ± 1, ±3 and ±7 GHz) channels that are less impacted by surface emissivity variations. Rain extent is determined using a number of scattering measures computed from linear combinations of the brightness temperature at the selected high frequency channels. Next, the ice water path parameter is estimated from a quadratic relationship with the brightness temperature at the 183 ± 7 GHz water vapor absorption channel and the cosine of the local zenith angle. Rain rate is computed from ice water path. Evaluation and monitoring of the new technique integrated with the existing algorithm has indicated that the upgraded algorithm is robust and superior to the existing one over coast.

A MODIS Dual Spectral Rain Algorithm

Abstract The Moderate Resolution Imaging Spectroradiometer (MODIS) dual spectral rain algorithm (MODRA) is developed for rain retrievals over the northern midlatitudes. The reflectance of the MODIS water vapor absorption channel at 1.38 μm (R1.38 μm) has a potential to represent the cloud-top height displayed by the brightness temperature (TB) of the MODIS channel at 11 μm, because of an excellent negative relationship (correlation coefficient ≤−0.9) between R1.38 μm and TB11 μm for optically thick clouds with reflectance (R0.65 μm) greater than 0.75. With a training rainfall dataset from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) aboard the same Aqua satellite platform, two MODIS channels (R1.38 μm and R0.65 μm) are applied to form multiregression curves to estimate daytime rainfall. Results demonstrate that the instantaneous rain rates from MODRA, independent AMSR-E rainfall products, and surface rain gauge measurements are consistent. This study explores a new way to estimate rainfall from MODIS water vapor and cloud channels. The resulting technique could be applied to other similar satellite instruments for rain retrievals.

A new concept on remote sensing of cirrus optical depth and effective ice particle size using strong water vapor absorption channels near 1.38 and 1.88 /spl mu/m

Techniques for retrieving cloud optical properties, i.e., the optical depths and particle size distributions, using atmospheric channels in the visible and near-infrared spectral regions are well established. For partially transparent thin cirrus clouds, these channels receive solar radiances scattered by the surface and lower level water clouds. Accurate retrieval of optical properties of thin cirrus clouds requires proper modeling of the effects from the surface and the lower level water clouds. In this paper, we describe a new concept using two strong water vapor absorption channels near 1.38 and 1.88 /spl mu/m, together with one window channel, for remote sensing of cirrus optical properties. Both the 1.38- and 1.88-/spl mu/m channels are highly sensitive in detecting the upper level cirrus clouds. Both channels receive little scattered solar radiances from the surface and lower level water clouds because of the strong water vapor absorption below cirrus. The 1.88-/spl mu/m channel is quite sensitive to changes in ice particle size distributions, while the 1.38-/spl mu/m channel is less sensitive. These properties allow for simultaneous retrievals of optical depths and particle size distributions of cirrus clouds with minimal contaminations from the surface and lower level water clouds. Preliminary tests of this new concept are made using hyperspectral imaging data collected with the Airborne Visible Infrared Imaging Spectrometer. The addition of a channel near 1.88 /spl mu/m to future multichannel meteorological satellite sensors would improve our ability in global remote sensing of cirrus optical properties.