Retrieval of Cirrus Microphysical Properties with a Suite of Algorithms for Airborne and Spaceborne Lidar, Radar, and Radiometer Data

2006 ◽  
Vol 45 (12) ◽  
pp. 1665-1689 ◽  
Author(s):  
Yuying Zhang ◽  
Gerald G. Mace
Keyword(s):  
Satellite Observation ◽  
Sensor Data ◽  
Regional Study ◽  
Remote Sensors ◽  
Microphysical Properties ◽  
Cloud Properties ◽  
Moderate Resolution ◽  
Atmospheric Radiation Measurement ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Ice Water

Abstract Algorithms are developed to convert data streams from multiple airborne and spaceborne remote sensors into layer-averaged cirrus bulk microphysical properties. Radiometers such as the Moderate-Resolution Imaging Spectroradiometer (MODIS) observe narrowband spectral radiances, and active remote sensors such as the lidar on the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite and the millimeter radar on CloudSat will provide vertical profiles of attenuated optical backscatter and radar reflectivity. Equivalent airborne remote sensors are also routinely flown on the NASA WB-57F and ER-2 aircraft. Algorithms designed to retrieve cirrus microphysical properties from remote sensor data must be able to handle the natural variability of cirrus that can range from optically thick layers that cause lidar attenuation to tenuous layers that are not detected by the cloud radar. An approach that is adopted here is to develop an algorithm suite that has internal consistency in its formulation and assumptions. The algorithm suite is developed around a forward model of the observations and is inverted for layer-mean cloud properties using a variational technique. The theoretical uncertainty in the retrieved ice water path retrieval is 40%–50%, and the uncertainty in the layer-mean particle size retrieval ranges from 50% to 90%. Two case studies from the Cirrus Regional Study of Tropical Anvils and Cirrus Layers (CRYSTAL) Florida Area Cirrus Experiment (FACE) field campaign as well as ground-based cases from the Atmospheric Radiation Measurement Program (ARM) are used to show the efficacy and error characteristics of the algorithms.

Assessment of Arctic Cloud Cover Anomalies in Atmospheric Reanalysis Products Using Satellite Data

2016 ◽  
Vol 29 (17) ◽  
pp. 6065-6083 ◽  
Author(s):  
Yinghui Liu ◽  
Jeffrey R. Key
Keyword(s):  
Cloud Cover ◽  
Climate Models ◽  
Global Climate ◽  
Correlation Coefficients ◽  
Cloud Amount ◽  
Satellite Observation ◽  
Skill Score ◽  
Global Climate Models ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract Cloud cover is one of the largest uncertainties in model predictions of the future Arctic climate. Previous studies have shown that cloud amounts in global climate models and atmospheric reanalyses vary widely and may have large biases. However, many climate studies are based on anomalies rather than absolute values, for which biases are less important. This study examines the performance of five atmospheric reanalysis products—ERA-Interim, MERRA, MERRA-2, NCEP R1, and NCEP R2—in depicting monthly mean Arctic cloud amount anomalies against Moderate Resolution Imaging Spectroradiometer (MODIS) satellite observations from 2000 to 2014 and against Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) observations from 2006 to 2014. All five reanalysis products exhibit biases in the mean cloud amount, especially in winter. The Gerrity skill score (GSS) and correlation analysis are used to quantify their performance in terms of interannual variations. Results show that ERA-Interim, MERRA, MERRA-2, and NCEP R2 perform similarly, with annual mean GSSs of 0.36/0.22, 0.31/0.24, 0.32/0.23, and 0.32/0.23 and annual mean correlation coefficients of 0.50/0.51, 0.43/0.54, 0.44/0.53, and 0.50/0.52 against MODIS/CALIPSO, indicating that the reanalysis datasets do exhibit some capability for depicting the monthly mean cloud amount anomalies. There are no significant differences in the overall performance of reanalysis products. They all perform best in July, August, and September and worst in November, December, and January. All reanalysis datasets have better performance over land than over ocean. This study identifies the magnitudes of errors in Arctic mean cloud amounts and anomalies and provides a useful tool for evaluating future improvements in the cloud schemes of reanalysis products.

scholarly journals Evapotranspiration from Remote Sensing to Improve the Swat Model in Eastern Amazonia

2015 ◽  
Vol 22 (4) ◽  
pp. 456-464 ◽  
Author(s):  
Adriano Marlison Leão de Sousa ◽  
Maria Isabel Vitorino ◽  
Nilza Maria dos Reis Castro ◽  
Marcel do Nascimento Botelho ◽  
Paulo Jorge Oliveira Ponte de Souza
Keyword(s):  
Hydrological Modeling ◽  
Swat Model ◽  
Assessment Tools ◽  
Flow Data ◽  
Remote Sensors ◽  
Araguaia River ◽  
Moderate Resolution ◽  
Model Adjustment ◽  
Daily Scale ◽  
Moderate Resolution Imaging Spectroradiometer

ABSTRACT In this study, we estimated the evapotranspiration from orbital images - MODIS (Moderate Resolution Imaging Spectroradiometer) for assimilation in the hydrological modeling of the SWAT (Soil Water Assessment Tools) model. The data used include the period between October 2003 and December 2006 of the sub-basin of the Lajeado River, located in the Tocantins-Araguaia River basin in Tocantins state. Overall, the results of the use of heat flows estimated by remote sensors in the SWAT model can be considered satisfactory. The values of the COE (coefficient of efficiency of Nash-Sutcliffe) ranged from -0.40 to 0.91 in the comparison with the daily flow data and from 0.17 to 0.77 with the monthly flow data, with the assimilation of evapotranspiration from orbital images. These results indicate benefit to the model adjustment due to improvement in the data assimilated of approximately 0.91 in the COE on daily scale and 0.60 in the CEO on monthly scale.

scholarly journals Estimating marine aerosol particle volume and number from Maritime Aerosol Network data

2012 ◽  
Vol 12 (6) ◽  
pp. 14959-15007
Author(s):  
A. M. Sayer ◽  
A. Smirnov ◽  
N. C. Hsu ◽  
L. A. Munchak ◽  
B. N. Holben
Keyword(s):  
Transport Model ◽  
Surface Concentration ◽  
Open Ocean ◽  
Similar Data ◽  
Particle Volume ◽  
Aerosol Composition ◽  
Microphysical Properties ◽  
Present Collection ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract. As well as spectral aerosol optical depth (AOD), aerosol composition and concentration (number, volume, or mass) are of interest for a variety of applications. However, remote sensing of these quantities is more difficult than for AOD, as it is more sensitive to assumptions relating to aerosol composition. This study uses spectral AOD measured on Maritime Aerosol Network (MAN) cruises, with the additional constraint of a microphysical model for unpolluted maritime aerosol based on analysis of Aerosol Robotic Network (AERONET) inversions, to estimate these quantities over open ocean. When the MAN data are subset to those likely to be comprised of maritime aerosol, number and volume concentrations obtained are physically reasonable. Attempts to estimate surface concentration from columnar abundance, however, are shown to be limited by uncertainties in vertical distribution. Columnar AOD at 550 nm and aerosol number for unpolluted maritime cases are also compared with Moderate Resolution Imaging Spectroradiometer (MODIS) data, for both the present Collection 5.1 and forthcoming Collection 6. MODIS provides a best-fitting retrieval solution, as well as the average for several different solutions, with different aerosol microphysical models. The "average solution" MODIS dataset agrees more closely with MAN than the "best solution" dataset. Terra tends to retrieve lower aerosol number than MAN, and Aqua higher, linked with differences in the aerosol models commonly chosen. Collection 6 AOD is likely to agree more closely with MAN over open ocean than Collection 5.1. In situations where spectral AOD is measured accurately, and aerosol microphysical properties are reasonably well-constrained, estimates of aerosol number and volume using MAN or similar data would provide for a greater variety of potential comparisons with aerosol properties derived from satellite or chemistry transport model data.

scholarly journals Aerosol Characteristics and Their Impact on the Himalayan Energy Budget

2021 ◽  
Vol 14 (1) ◽  
pp. 179
Author(s):  
Kesar Chand ◽  
Jagdish Chandra Kuniyal ◽  
Shruti Kanga ◽  
Raj Paul Guleria ◽  
Gowhar Meraj ◽  
...  
Keyword(s):  
Solar Radiation ◽  
Radiative Forcing ◽  
Satellite Observation ◽  
Transfer Model ◽  
Hysplit Model ◽  
Aerosol Radiative Forcing ◽  
Alpine Regions ◽  
Himalayan Glaciers ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer

The extensive work on the increasing burden of aerosols and resultant climate implications shows a matter of great concern. In this study, we investigate the aerosol optical depth (AOD) variations in the Indian Himalayan Region (IHR) between its plains and alpine regions and the corresponding consequences on the energy balance on the Himalayan glaciers. For this purpose, AOD data from Moderate Resolution Imaging Spectroradiometer (MODIS, MOD-L3), Aerosol Robotic Network (AERONET), India, and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) were analyzed. Aerosol radiative forcing (ARF) was assessed using the atmospheric radiation transfer model (RTM) integrated into AERONET inversion code based on the Discrete Ordinate Radiative Transfer (DISORT) module. Further, air mass trajectory over the entire IHR was analyzed using a hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model. We estimated that between 2001 and 2015, the monthly average ARF at the surface (ARFSFC), top of the atmosphere (ARFTOA), and atmosphere (ARFATM) were −89.6 ± 18.6 Wm−2, −25.2 ± 6.8 Wm−2, and +64.4 ± 16.5 Wm−2, respectively. We observed that during dust aerosol transport days, the ARFSFC and TOA changed by −112.2 and −40.7 Wm−2, respectively, compared with low aerosol loading days, thereby accounting for the decrease in the solar radiation by 207% reaching the surface. This substantial decrease in the solar radiation reaching the Earth’s surface increases the heating rate in the atmosphere by 3.1-fold, thereby acting as an additional forcing factor for accelerated melting of the snow and glacier resources of the IHR.

scholarly journals A Study of the Distribution and Variability of Cloud Water Using ISCCP, SSM/I Cloud Product, and Reanalysis Datasets

2014 ◽  
Vol 27 (9) ◽  
pp. 3114-3128 ◽  
Author(s):  
Zhiwei Heng ◽  
Yunfei Fu ◽  
Guosheng Liu ◽  
Renjun Zhou ◽  
Yu Wang ◽  
...  
Keyword(s):  
Cloud Water ◽  
Positive Anomaly ◽  
Liquid Water Path ◽  
Water Path ◽  
The North ◽  
Increasing Trend ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Microwave Imager ◽  
Ice Water

Abstract In this paper, the global distribution of cloud water based on International Satellite Cloud Climatology Project (ISCCP), Moderate Resolution Imaging Spectroradiometer (MODIS), CloudSat Cloud Profiling Radar (CPR), European Center for Medium-Range Weather Forecasts Interim Re-Analysis (ERA-Interim), and Climate Forecast System Reanalysis (CFSR) datasets is presented, and the variability of cloud water from ISCCP, the Special Sensor Microwave Imager (SSM/I), ERA-Interim, and CFSR data over the time period of 1995 through 2009 is discussed. The results show noticeable differences in cloud water over land and over ocean, as well as latitudinal variations. Large values of cloud water are mainly distributed over the North Pacific and Atlantic Oceans, eastern ITCZ, regions off the west coast of the continents as well as tropical rain forest. Cloud water path (CWP), liquid water path (LWP), and ice water path (IWP) from these datasets show a relatively good agreement in distributions and zonal means. The results of trend analyzing show an increasing trend in CWP, and also a significant increasing trend of LWP can be found in the dataset of ISCCP, ERA-Interim, and CFSR over the ocean. Besides the long-term variation trend, rises of cloud water are found when temperature and water vapor exhibit a positive anomaly. EOF analyses are also applied to the anomalies of cloud water, the first dominate mode of CWP and IWP are similar, and a phase change can be found in the LWP time coefficient around 1999 in ISCCP and CFSR and around 2002 in ERA-Interim.

scholarly journals CALIPSO IIR Version 2 Level 1b calibrated radiances: analysis and reduction of residual biases in the Northern Hemisphere

2018 ◽  
Vol 11 (4) ◽  
pp. 2485-2500 ◽  
Author(s):  
Anne Garnier ◽  
Thierry Trémas ◽  
Jacques Pelon ◽  
Kam-Pui Lee ◽  
Delphine Nobileau ◽  
...  
Keyword(s):  
Satellite Observation ◽  
Calibration Procedure ◽  
Latitude Range ◽  
Brightness Temperatures ◽  
Depth Analysis ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
The Individual ◽  
Semi Empirical ◽  
Global Bias

Abstract. Version 2 of the Level 1b calibrated radiances of the Imaging Infrared Radiometer (IIR) on board the Cloud-Aerosol Lidar and Infrared Satellite Observation (CALIPSO) satellite has been released recently. This new version incorporates corrections of small but systematic seasonal calibration biases previously revealed in Version 1 data products mostly north of 30∘ N. These biases – of different amplitudes in the three IIR channels 8.65 µm (IIR1), 10.6 µm (IIR2), and 12.05 µm (IIR3) – were made apparent by a striping effect in images of IIR inter-channel brightness temperature differences (BTDs) and through seasonal warm biases of nighttime IIR brightness temperatures in the 30–60∘ N latitude range. The latter were highlighted through observed and simulated comparisons with similar channels of the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Aqua spacecraft. To characterize the calibration biases affecting Version 1 data, a semi-empirical approach is developed, which is based on the in-depth analysis of the IIR internal calibration procedure in conjunction with observations such as statistical comparisons with similar MODIS/Aqua channels. Two types of calibration biases are revealed: an equalization bias affecting part of the individual IIR images and a global bias affecting the radiometric level of each image. These biases are observed only when the temperature of the instrument increases, and they are found to be functions of elapsed time since night-to-day transition, regardless of the season. Correction coefficients of Version 1 radiances could thus be defined and implemented in the Version 2 code. As a result, the striping effect seen in Version 1 is significantly attenuated in Version 2. Systematic discrepancies between nighttime and daytime IIR–MODIS BTDs in the 30–60∘ N latitude range in summer are reduced from 0.2 K in Version 1 to 0.1 K in Version 2 for IIR1–MODIS29. For IIR2–MODIS31 and IIR3–MODIS32, they are reduced from 0.4 K to close to zero, except for IIR3–MODIS32 in June, where the night-minus-day difference is around −0.1 K.

scholarly journals Synergy of lidar and passive remote sensor data for retrieving profiles of microphysical properties of non-spherical particles

2018 ◽  
Vol 176 ◽  
pp. 08001
Author(s):  
Alexei Kolgotin ◽  
Detlef Müller ◽  
Eduard Chemyakin ◽  
Anton Romanov
Keyword(s):  
Light Scattering ◽  
Phase Function ◽  
Sensor Data ◽  
Spherical Particles ◽  
Lidar Data ◽  
Scattering Model ◽  
Remote Sensors ◽  
Microphysical Properties ◽  
Remote Sensor

In this study we explore how the combination of 3 backscatter and 2 extinction lidar data with data that can be collected with ground-based and space-borne passive remote sensors, e.g. phase function coefficients which can be derived at various measurement wavelengths and scattering angles can result in improved profiles of particle microphysical properties. The algorithm is based on a light-scattering model that uses a mixture of spheres and randomly oriented spheroids.

scholarly journals Identifying drought events in sugarcane using drought indices derived from Modis sensor

2017 ◽  
Vol 52 (11) ◽  
pp. 1063-1071 ◽  
Author(s):  
Michelle Cristina Araujo Picoli ◽  
Daniel Garbellini Duft ◽  
Pedro Gerber Machado
Keyword(s):  
Near Infrared ◽  
Sensor Data ◽  
Drought Indices ◽  
Spectral Indices ◽  
Seasonal Fluctuations ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Aqua Satellite ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Shortwave Infrared

Abstract: The objective of this work was to evaluate the potential of several spectral indices, used on moderate resolution imaging spectroradiometer (Modis) images, in identifying drought events in sugarcane. Images of Terra and Aqua satellites were used to calculate the spectral indices, using visible (red), near infrared, and shortwave infrared bands, and eight indices were selected: NDVI, EVI2, GVMI, NDI6, NDI7, NDWI, SRWI, and MSI. The indices were calculated using images between October and April of the crop years 2007/08, 2008/09, 2009/10, and 2013/14. These indices were then correlated with the standardized precipitation-evapotranspiration index (SPEI), calculated for 1, 3, and 6 months. Four of them had significant correlations with SPEI: GVMI, MSI, NDI7, and NDWI. Spectral indices from Modis sensor on board the Aqua satellite (MYD) were more suited for drought detection, and March provided the most relevant indices for that purpose. Drought indices calculated from Modis sensor data are effective for detecting sugarcane drought events, besides being able to indicate seasonal fluctuations.

scholarly journals A critical look at spatial scale choices in satellite-based aerosol indirect effect studies

2010 ◽  
Vol 10 (23) ◽  
pp. 11459-11470 ◽  
Author(s):  
B. S. Grandey ◽  
P. Stier
Keyword(s):  
Radiative Forcing ◽  
Spatial Scales ◽  
Cloud Droplet ◽  
Spatial Variations ◽  
Opposite Pattern ◽  
Cloud Properties ◽  
Methodological Errors ◽  
Albedo Effect ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract. Analysing satellite datasets over large regions may introduce spurious relationships between aerosol and cloud properties due to spatial variations in aerosol type, cloud regime and synoptic regime climatologies. Using MODerate resolution Imaging Spectroradiometer data, we calculate relationships between aerosol optical depth τa derived liquid cloud droplet effective number concentration Ne and liquid cloud droplet effective radius re at different spatial scales. Generally, positive values of dlnNedlnτa are found for ocean regions, whilst negative values occur for many land regions. The spatial distribution of dlnredlnτa shows approximately the opposite pattern, with generally postive values for land regions and negative values for ocean regions. We find that for region sizes larger than 4° × 4°, spurious spatial variations in retrieved cloud and aerosol properties can introduce widespread significant errors to calculations of dlnNedlnτa and dlnredlnτa. For regions on the scale of 60° × 60°, these methodological errors may lead to an overestimate in global cloud albedo effect radiative forcing of order 80% relative to that calculated for regions on the scale of 1° × 1°.

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