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Qing Yue, K. N. Liou, S. C. Ou, B. H. Kahn, P. Yang, and G. G. Mace

Abstract

A thin cirrus cloud thermal infrared radiative transfer model has been developed for application to cloudy satellite data assimilation. This radiation model was constructed by combining the Optical Path Transmittance (OPTRAN) model, developed for the speedy calculation of transmittances in clear atmospheres, and a thin cirrus cloud parameterization using a number of observed ice crystal size and shape distributions. Numerical simulations show that cirrus cloudy radiances in the 800–1130-cm−1 thermal infrared window are sufficiently sensitive to variations in cirrus optical depth and ice crystal size as well as in ice crystal shape if appropriate habit distribution models are selected a priori for analysis. The parameterization model has been applied to the Atmospheric Infrared Sounder (AIRS) on board the Aqua satellite to interpret clear and thin cirrus spectra observed in the thermal infrared window. Five clear and 29 thin cirrus cases at nighttime over and near the Atmospheric Radiation Measurement program (ARM) tropical western Pacific (TWP) Manus Island and Nauru Island sites have been chosen for this study. A χ2-minimization program was employed to infer the cirrus optical depth and ice crystal size and shape from the observed AIRS spectra. Independent validation shows that the AIRS-inferred cloud parameters are consistent with those determined from collocated ground-based millimeter-wave cloud radar measurements. The coupled thin cirrus radiative transfer parameterization and OPTRAN, if combined with a reliable thin cirrus detection scheme, can be effectively used to enhance the AIRS data volume for data assimilation in numerical weather prediction models.

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Qing Yue, Brian H. Kahn, Eric J. Fetzer, Sun Wong, Xianglei Huang, and Mathias Schreier

Abstract

Observations from multiple sensors on the NASA Aqua satellite are used to estimate the temporal and spatial variability of short-term cloud responses (CR) and cloud feedbacks λ for different cloud types, with respect to the interannual variability within the A-Train era (July 2002–June 2017). Short-term cloud feedbacks by cloud type are investigated both globally and locally by three different definitions in the literature: 1) the global-mean cloud feedback parameter λ GG from regressing the global-mean cloud-induced TOA radiation anomaly ΔR G with the global-mean surface temperature change ΔT GS; 2) the local feedback parameter λ LL from regressing the local ΔR with the local surface temperature change ΔT S; and 3) the local feedback parameter λ GL from regressing global ΔR G with local ΔT S. Observations show significant temporal variability in the magnitudes and spatial patterns in λ GG and λ GL, whereas λ LL remains essentially time invariant for different cloud types. The global-mean net λ GG exhibits a gradual transition from negative to positive in the A-Train era due to a less negative λ GG from low clouds and an increased positive λ GG from high clouds over the warm pool region associated with the 2015/16 strong El Niño event. Strong temporal variability in λ GL is intrinsically linked to its dependence on global ΔR G, and the scaling of λ GL with surface temperature change patterns to obtain global feedback λ GG does not hold. Despite the shortness of the A-Train record, statistically robust signals can be obtained for different cloud types and regions of interest.

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Colten A. Peterson, Qing Yue, Brian H. Kahn, Eric Fetzer, and Xianglei Huang

Abstract

Cloud phase retrievals from the Atmospheric Infrared Sounder (AIRS) are evaluated against combined CloudSat–CALIPSO (CCL) observations using four years of data (2007–10) over the Arctic Ocean. AIRS cloud phase is evaluated over sea ice and open ocean separately using collocated CCL and AIRS fields of view (FOVs). In addition, AIRS and CCL cloud phase occurrences are evaluated seasonally, zonally, and with respect to total column water vapor (TCWV) and the temperature difference between 1000 and 300 hPa (ΔT 1000−300). Last, collocated MODIS cloud information is implemented in a 1-month case study to assess the relationship between AIRS and CCL phase decisions, cloud cover, and cloud phase throughout the AIRS FOV. Depending on the surface type, AIRS classification skill for single-layer ice and liquid-phase clouds is over the ranges of 85%–95% and 22%–32%, respectively. Most unknown and liquid AIRS phase classifications correspond to mixed-phase clouds. AIRS ice-phase relative occurrence is biased low relative to CCL. However, the liquid-phase relative occurrence is similar between the two instruments. When compared with the CCL climatology, AIRS accurately represents the seasonal cycle of liquid and ice cloud phase across the Arctic as well as the relationship between cloud phase and TCWV and ΔT 1000−300 regime in some cases. The more heterogeneous the MODIS cloud macrophysical properties within an AIRS FOV are, the more likely it is that the AIRS FOV is classified as unknown phase.

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Qing Yue, Eric J. Fetzer, Brian H. Kahn, Sun Wong, Gerald Manipon, Alexandre Guillaume, and Brian Wilson

Abstract

The 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 total biases in the AIRS data and are largest in the presence of deep convective (DC) and nimbostratus (Ns) clouds. Systematic cold and dry biases are found throughout the free troposphere for DC and Ns. Somewhat larger biases are found over land and in the midlatitudes than over the oceans and in the tropics, respectively. Tropical and oceanic regions generally have a smaller CRMSD than the midlatitudes and over land, suggesting agreement of T and q variability between AIRS and ECMWF in these regions. The magnitude of CRMSD is also strongly dependent on cloud type.

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Qing Yue, Brian H. Kahn, Eric J. Fetzer, Mathias Schreier, Sun Wong, Xiuhong Chen, and Xianglei Huang

Abstract

The 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 show the TOA radiative sensitivity of cloud types to unit cloud fraction change as observed by the A-Train. Therefore, a combination of observation-based CRKs with cloud changes observed by these instruments over time will provide an estimate of the short-term cloud feedback by maintaining consistency between CRKs and cloud responses to climate variability.

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Yaohui Li, Xing Yuan, Hongsheng Zhang, Runyuan Wang, Chenghai Wang, Xianhong Meng, Zhiqiang Zhang, Shanshan Wang, Yang Yang, Bo Han, Kai Zhang, Xiaoping Wang, Hong Zhao, Guangsheng Zhou, Qiang Zhang, Qing He, Ni Guo, Wei Hou, Cunjie Zhang, Guoju Xiao, Xuying Sun, Ping Yue, Sha Sha, Heling Wang, Tiejun Zhang, Jinsong Wang, and Yubi Yao

Abstract

A major experimental drought research project entitled “Mechanisms and Early Warning of Drought Disasters over Northern China” (DroughtEX_China) was launched by the Ministry of Science and Technology of China in 2015. The objective of DroughtEX_China is to investigate drought disaster mechanisms and provide early-warning information via multisource observations and multiscale modeling. Since the implementation of DroughtEX_China, a comprehensive V-shape in situ observation network has been established to integrate different observational experiment systems for different landscapes, including crops in northern China. In this article, we introduce the experimental area, observational network configuration, ground- and air-based observing/testing facilities, implementation scheme, and data management procedures and sharing policy. The preliminary observational and numerical experimental results show that the following are important processes for understanding and modeling drought disasters over arid and semiarid regions: 1) the soil water vapor–heat interactions that affect surface soil moisture variability, 2) the effect of intermittent turbulence on boundary layer energy exchange, 3) the drought–albedo feedback, and 4) the transition from stomatal to nonstomatal control of plant photosynthesis with increasing drought severity. A prototype of a drought monitoring and forecasting system developed from coupled hydroclimate prediction models and an integrated multisource drought information platform is also briefly introduced. DroughtEX_China lasted for four years (i.e., 2015–18) and its implementation now provides regional drought monitoring and forecasting, risk assessment information, and a multisource data-sharing platform for drought adaptation over northern China, contributing to the global drought information system (GDIS).

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James Edson, Timothy Crawford, Jerry Crescenti, Tom Farrar, Nelson Frew, Greg Gerbi, Costas Helmis, Tihomir Hristov, Djamal Khelif, Andrew Jessup, Haf Jonsson, Ming Li, Larry Mahrt, Wade McGillis, Albert Plueddemann, Lian Shen, Eric Skyllingstad, Tim Stanton, Peter Sullivan, Jielun Sun, John Trowbridge, Dean Vickers, Shouping Wang, Qing Wang, Robert Weller, John Wilkin, Albert J. Williams III, D. K. P. Yue, and Chris Zappa

The Office of Naval Research's Coupled Boundary Layers and Air–Sea Transfer (CBLAST) program is being conducted to investigate the processes that couple the marine boundary layers and govern the exchange of heat, mass, and momentum across the air–sea interface. CBLAST-LOW was designed to investigate these processes at the low-wind extreme where the processes are often driven or strongly modulated by buoyant forcing. The focus was on conditions ranging from negligible wind stress, where buoyant forcing dominates, up to wind speeds where wave breaking and Langmuir circulations play a significant role in the exchange processes. The field program provided observations from a suite of platforms deployed in the coastal ocean south of Martha's Vineyard. Highlights from the measurement campaigns include direct measurement of the momentum and heat fluxes on both sides of the air–sea interface using a specially constructed Air–Sea Interaction Tower (ASIT), and quantification of regional oceanic variability over scales of O(1–104 mm) using a mesoscale mooring array, aircraft-borne remote sensors, drifters, and ship surveys. To our knowledge, the former represents the first successful attempt to directly and simultaneously measure the heat and momentum exchange on both sides of the air–sea interface. The latter provided a 3D picture of the oceanic boundary layer during the month-long main experiment. These observations have been combined with numerical models and direct numerical and large-eddy simulations to investigate the processes that couple the atmosphere and ocean under these conditions. For example, the oceanic measurements have been used in the Regional Ocean Modeling System (ROMS) to investigate the 3D evolution of regional ocean thermal stratification. The ultimate goal of these investigations is to incorporate improved parameterizations of these processes in coupled models such as the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) to improve marine forecasts of wind, waves, and currents.

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