Qing Yue

Cloud-State-Dependent Sampling in AIRS Observations Based onCloudSatCloud Classification

AbstractThe precision, accuracy, and potential sampling biases of temperature T and water vapor q vertical profiles obtained by satellite infrared sounding instruments are highly cloud-state dependent and poorly quantified. The authors describe progress toward a comprehensive T and q climatology derived from the Atmospheric Infrared Sounder (AIRS) suite that is a function of cloud state based on collocated CloudSat observations. The AIRS sampling rates, biases, and center root-mean-square differences (CRMSD) are determined through comparisons of pixel-scale collocated ECMWF model analysis data. The results show that AIRS provides a realistic representation of most meteorological regimes in most geographical regions, including those dominated by high thin cirrus and shallow boundary layer clouds. The mean AIRS observational biases relative to the ECMWF analysis between the surface and 200 hPa are within ±1 K in T and from −1 to +0.5 g kg−1 in q. Biases because of cloud-state-dependent sampling dominate the...

Tightening of tropical ascent and high clouds key to precipitation change in a warmer climate

The change of global-mean precipitation under global warming and interannual variability is predominantly controlled by the change of atmospheric longwave radiative cooling. Here we show that tightening of the ascending branch of the Hadley Circulation coupled with a decrease in tropical high cloud fraction is key in modulating precipitation response to surface warming. The magnitude of high cloud shrinkage is a primary contributor to the intermodel spread in the changes of tropical-mean outgoing longwave radiation (OLR) and global-mean precipitation per unit surface warming (dP/dTs) for both interannual variability and global warming. Compared to observations, most Coupled Model Inter-comparison Project Phase 5 models underestimate the rates of interannual tropical-mean dOLR/dTs and global-mean dP/dTs, consistent with the muted tropical high cloud shrinkage. We find that the five models that agree with the observation-based interannual dP/dTs all predict dP/dTs under global warming higher than the ensemble mean dP/dTs from the ∼20 models analysed in this study.

Cloud‐induced uncertainties in AIRS and ECMWF temperature and specific humidity

The uncertainties of the Atmospheric Infrared Sounder (AIRS) Level 2 version 6 specific humidity (q) and temperature (T) retrievals are quantified as functions of cloud types by comparison against Integrated Global Radiosonde Archive radiosonde measurements. The cloud types contained in an AIRS/Advanced Microwave Sounding Unit footprint are identified by collocated Moderate Resolution Imaging Spectroradiometer retrieved cloud optical depth (COD) and cloud top pressure. We also report results of similar validation of q and T from European Centre for Medium-Range Weather Forecasts (ECMWF) forecasts (EC) and retrievals from the AIRS Neural Network (NNW), which are used as the initial state for AIRS V6 physical retrievals. Differences caused by the variation in the measurement locations and times are estimated using EC, and all the comparisons of data sets against radiosonde measurements are corrected by these estimated differences. We report in detail the validation results for AIRS GOOD quality control, which is used for the AIRS Level 3 climate products. AIRS GOOD quality q reduces the dry biases inherited from the NNW in the middle troposphere under thin clouds but enhances dry biases in thick clouds throughout the troposphere (reaching −30% at 850 hPa near deep convective clouds), likely because the information contained in AIRS retrievals is obtained in cloud-cleared areas or above clouds within the field of regard. EC has small moist biases (~5–10%), which are within the uncertainty of radiosonde measurements, in thin and high clouds. Temperature biases of all data are within ±1 K at altitudes above the 700 hPa level but increase with decreasing altitude. Cloud-cleared retrievals lead to large AIRS cold biases (reaching about −2 K) in the lower troposphere for large COD, enhancing the cold biases inherited from the NNW. Consequently, AIRS GOOD quality T root-mean-squared errors (RMSEs) are slightly smaller than the NNW errors in thin clouds (1.5–2.5 K) but slightly larger than the NNW errors for thick COD (reaching 3.5 K near the surface). The AIRS BEST quality control retains retrievals with uncertainties closer to those of the NNW. The AIRS error estimates reported in the L2 product tend to underestimate the precision (RMSE) implied by comparisons to the radiosonde measurements and do not reflect the observed cloud dependency of uncertainties.

Observation-Based Longwave Cloud Radiative Kernels Derived from the A-Train

AbstractThe authors present a new method to derive both the broadband and spectral longwave observation-based cloud radiative kernels (CRKs) using cloud radiative forcing (CRF) and cloud fraction (CF) for different cloud types using multisensor A-Train observations and MERRA data collocated on the pixel scale. Both observation-based CRKs and model-based CRKs derived from the Fu–Liou radiative transfer model are shown. Good agreement between observation- and model-derived CRKs is found for optically thick clouds. For optically thin clouds, the observation-based CRKs show a larger radiative sensitivity at TOA to cloud-cover change than model-derived CRKs. Four types of possible uncertainties in the observed CRKs are investigated: 1) uncertainties in Moderate Resolution Imaging Spectroradiometer cloud properties, 2) the contributions of clear-sky changes to the CRF, 3) the assumptions regarding clear-sky thresholds in the observations, and 4) the assumption of a single-layer cloud. The observation-based CRKs...

Validation of MODIS cloud mask and multilayer flag using CloudSat‐CALIPSO cloud profiles and a cross‐reference of their cloud classifications

Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 cloud observations (MYD06) at 1 km are collocated with daytime CloudSat-Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) (C-C) cloud vertical structures (2B-CLDCLASS-LIDAR). For 2007–2010, over 267 million C-C cloud profiles are used to (1) validate MODIS cloud mask and cloud multilayer flag and (2) cross-reference between C-C cloud types and MODIS cloud regimes defined by joint histograms of cloud top pressure (CTP) and cloud optical depth (τ). Globally, of total observations, C-C reports 27.1% clear and 72.9% cloudy, whereas MODIS reports 30.0% confidently clear and 58.7% confidently cloudy, with the rest 7.1% as probably clear and 4.2% as probably cloudy. Agreement between MODIS and C-C is 77.8%, with 20.9% showing both clear and 56.9% showing both cloudy. The 9.1% of observations are clear in MODIS but cloudy in C-C, indicating clouds missed by MODIS; 1.8% of observations are cloudy in MODIS but clear in C-C, likely due to aerosol/dust or surface snow layers misidentified by MODIS. C-C reports 47.4/25.5% single-layer/multilayer clouds, while MODIS reports 26.7/14.0%. For C-C single-layer clouds, ~90% of tropical MODIS high (CTP   23) clouds are recognized as deep convective in C-C. Approximately 70% of MODIS low-level (CTP > 680 hPa) clouds are classified as stratocumulus in C-C regardless of region and optical thickness. No systematic relationship exists between MODIS middle-level (680 < CTP < 440 hPa) clouds and C-C cloud types, largely due to different definitions adopted.

Characterizing tropical Pacific water vapor and radiative biases in CMIP5 GCMs: Observation‐based analyses and a snow and radiation interaction sensitivity experiment

Significant systematic biases in the moisture fields within the tropical Pacific trade wind regions are found in the Coupled Model Intercomparison Project (CMIP3/CMIP5) against profile and total column water vapor (TotWV) estimates from the Atmospheric Infrared Sounder and TotWV from the Special Sensor Microwave/Imager. Positive moisture biases occur in conjunction with significant biases of eastward low-level moisture convergence north of the South Pacific Convergence Zone and south of the Intertropical Convergence Zone—the V-shaped regions. The excessive moisture there is associated with overestimates of reflected upward shortwave (RSUT), underestimates of outgoing longwave radiation (RLUT) at the top of atmosphere (TOA), and underestimates of downward shortwave flux at the surface (RSDS) compared to Clouds and the Earths Energy System, Energy Balance and Filled data. We characterize the impacts of falling snow and its radiation interaction, which are not included in most CMIP5 models, on the moisture fields using the National Center for Atmospheric Research-coupled global climate model (GCM). A number of differences in the model simulation without snow-radiation interactions are consistent with biases in the CMIP5 simulations. These include effective low-level eastward/southeastward wind and surface wind stress anomalies, and an increase in TotWV, vertical profile of moisture, and cloud amounts in the V-shaped region. The anomalous water vapor and cloud amount might be associated with the model increase of RSUT and decrease of RLUT at TOA and decreased RSDS in clear and all sky in these regions. These findings hint at the importance of water vapor-radiation interactions in the CMIPS/CMIP5 model simulations that exclude the radiative effect of snow.

On the response of MODIS cloud coverage to global mean surface air temperature

The global surface temperature change (ΔTs) mediated cloud cover response is directly related to cloud-climate feedback. Using satellite remote sensing data to relate cloud and climate requires a well-calibrated, stable, and consistent long-term cloud data record. The Collection 5.1 (C5) MODerate resolution Imaging Spectroradiometer (MODIS) cloud observations have been widely used for this purpose. However, the MODIS data quality varies greatly with the surface type, spectral region, cloud type, and time periods of study, which calls for additional caution when applying such data to studies on cloud cover temporal trends and variability. Using 15 years of cloud observations made by Terra and Aqua MODIS, we analyze the ΔTs-mediated cloud cover response for different cloud types by linearly regressing the monthly anomaly of cloud cover (ΔC) with the monthly anomaly of global Ts. The Collection 6 (C6) Aqua data exhibits a similar cloud response to the long-term counterpart simulated by advanced climate models. A robust increase in altitude with increasing ΔTs is found for high clouds, while a robust decrease of ΔC is noticed for optically thick low clouds. The large differences between C5 and C6 results are from improvements in calibration and cloud retrieval algorithms. The large positive cloud cover responses with data after 2010 and the strong sensitivity to time period obtained from the Terra (C5 and C6) data are likely due to calibration drift that has not been corrected, suggesting that the previous estimate of the short-term cloud cover response from the these data should be revisited.

Retrieval of Cirrus Cloud Properties From the Atmospheric Infrared Sounder: The K-Coefficient Approach Using Cloud-Cleared Radiances as Input

We have developed a k-coefficient retrieval approach for Atmospheric Infrared Sounder (AIRS) observations, using AIRS cloud-cleared radiances (ACCRs) as input. This new approach takes advantage of the available ACCR, reduces computational expense, offers an efficient and accurate cirrus cloud retrieval alternative for hyperspectral infrared (IR) observations, and is potentially applicable to the compilation of a long-term cirrus cloud climatology from hyperspectral IR observations. The retrieval combines a lookup-table method coupled to a residual minimization scheme using observed cloudy and cloud-cleared AIRS radiances as input. Six AIRS channels between 766 and 832 cm-1 with minimal water vapor absorption/emission have been selected, and their spectral radiances have been demonstrated to be sensitive to both cirrus cloud optical depth (τc) and ice crystal effective particle size (De). The capability of the k-coefficient approach is demonstrated by comparison with a more accurate retrieval program, which combines the delta-four stream (D4S) approximation with the currently operational Stand-alone AIRS Radiative Transfer Algorithm (SARTA). The distribution patterns and the range of retrieved cloud parameters from the k-coefficient approach are nearly identical to those from SARTA+D4S retrievals, with minor differences traced to uncertainties in parameterized cloudy radiances in the k-coefficient approach and in the ACCR. The k -coefficient approach has also been applied to four AIRS granules over North Central China, Mongolia, and Siberia containing a significant presence of cirrus clouds, and its results are quantitatively compared to simultaneous Moderate Resolution Imaging Spectroradiometer/Aqua cirrus cloud retrievals. Finally, AIRS retrieved τc and De are consistent with the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and CloudSat derived values for semitransparent cirrus clouds, with more significant differences in thicker cirrus and multilayer clouds.

A Simulation of Ice Cloud Particle Size, Humidity and Temperature Measurements from the TWICE CubeSat

Abstract This paper describes a forward radiative transfer model and retrieval system (FMRS) for the Tropospheric Water and cloud ICE (TWICE) CubeSat instrument. We use the FMRS to simulate radiances for the TWICEs 14 millimeter‐ and submillimeter‐wavelength channels for a tropical atmospheric state produced by a Weather Research and Forecasting model simulation. We also perform simultaneous retrievals of cloud ice particle size, ice water content (IWC), water vapor content (H2O), and temperature from the simulated TWICE radiances using the FMRS. We show that the TWICE instrument is capable of retrieving ice particle size in the range of ~50–1000 μm in mass mean effective diameter with approximately 50% uncertainty. The uncertainties of other retrievals from TWICE are about 1 K for temperature, 50% for IWC, and 20% for H2O.

Sounding science at the Jet Propulsion Laboratory

The Jet Propulsion Laboratory (JPL) is best known for planetary exploration but is also heavily involved in Earth science and has in recent years become one of the premier centers for atmospheric science related to infrared and microwave satellite sounders such as the Atmospheric Infrared Sounder (AIRS), the Advanced Microwave Sounding Unit (AMSU) and the Advanced Technology Microwave Sounder (ATMS), as well as aircraft based microwave sounders such as the High Altitude MMIC Sounding Radiometer (HAMSR) and the development of future sounders such as an infrared CubeSat system (CIRAS) and a geostationary microwave sounder (GeoSTAR). We give a brief overview of these sensors and focus on the development and assessment of sounder data products, which include vertical profiles of temperature and water vapor, cloud and surface parameters, and in the case of infrared sounders also trace gas estimates and for microwave sounders precipitation as well. The baseline AIRS data product “retrieval system” was developed by the AIRS science team and has been undergoing continuous maintenance and upgrade in close collaboration with the sounder team at JPL. To support that process, the JPL team has developed a broad range of assessment tools and techniques, which can be applied to data from other sounders as well and can range from simple “sanity check” analysis to thorough “validation” analysis. An example of the less complex testing is the preliminary assessment of products generated by new retrieval systems operating on data from the Cross-track Infrared Sounder (CrIS) and the Advanced Technology Microwave Sounder (ATMS) flying on the Suomi NPP and JPSS satellites. These retrieval systems are developed by individual investigators funded by NASA research grants and are delivered to a Sounder “Science Investigator Processing System” (SIPS) located at JPL for integration, testing and delivery to a NASA data processing center and eventual release to the public, but only limited resources are available to the SIPS for the assessment, which therefore must be relatively superficial. An example of thorough assessment is the quantification of the impact on AIRS products of the failure of the AMSU-A2 microwave sounder 2 years ago. The baseline AIRS retrieval system used initially data from the companion microwave sounders, the Humidity Sounder for Brazil (HSB), AMSU-A1 and AMSU-A2, to provide a “first guess” and support “cloud clearing”. As these instruments suddenly failed (HSB) or gradually deteriorated (AMSU), some effort was devoted to develop a version that did not depend on microwave data. It was considered somewhat inferior to the baseline system and was kept in reserve and therefore not fully assessed. When AMSU-A2 failed, this AIRS-only system became the primary version, and a substantial effort was undertaken to fully assess its performance. We discuss details of that assessment. These capabilities have resulted from substantial investments NASA has made over the years in support of AIRS and can now be applied to next-generation systems as well.