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R. S. Lindzen, A. Y. Hou, and B. F. Farrell

Abstract

The role of the parameterization of vertical convection in calculating the climate impact of doubling CO2 is assessed using both one-dimensional radiative-convective vertical models and in the latitude-dependent Hadley-baroclinic model of Lindzen and Farrell (1980). Both the conventional 6.5 K km−1 and the moist-adiabat adjustments are compared with a physically-based, cumulus-type parameterization. The model with parameterized cumulus convection has much less sensitivity than the 6.5 K km−1 adjustment model at low latitudes, a result that can be to some extent imitated by the moist-adiabat adjustment model. However, when averaged over the globe, the use of the cumulus-type parameterization in a climate model reduces sensitivity only ∼34% relative to models using 6.5 K km−1 convective adjustment. Interestingly, the use of the cumulus-type parameterization appears to eliminate the possibility of a runaway greenhouse.

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V. M. Krasnopolsky, M. S. Fox-Rabinovitz, Y. T. Hou, S. J. Lord, and A. A. Belochitski

Abstract

The approach to accurate and fast-calculating model physics using neural network emulations was previously developed by the authors for both longwave and shortwave radiation parameterizations or the full model radiation, which is the most time-consuming component of model physics. It was successfully tested for a moderate-resolution uncoupled NCAR Community Atmospheric Model (CAM) that is driven by climatological SST for a decadal climate simulation mode. In this study, the approach has been further developed and implemented into the NCEP coupled Climate Forecast System (CFS) with significantly higher resolution and time-dependent CO2. The higher complexity of NCEP CFS required further adjustments to the neural network emulation methodology. Validation of the approach for the NCEP CFS has been performed through a decadal climate simulation and seasonal predictions. The developed highly-accurate neural network emulations of longwave and shortwave radiation parameterizations are, on average, 16 and 60 times faster than the original/control longwave and shortwave radiation parameterizations, respectively. The authors present a detailed comparison of parallel decadal climate simulations and seasonal predictions performed with the original NCEP model radiation parameterizations and with their neural network emulations. The differences between the parallel runs are overall within or less than the observation errors and uncertainties of reanalysis. Moreover, the differences (both in terms of bias and RMSE) are of a similar magnitude as the model’s internal variability. These results justify the practical use of efficient neural network emulations of full model radiation for climate simulations and seasonal predictions.

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Yu-Tai Hou, Kenneth A. Campana, Kenneth E. Mitchell, Shi-Keng Yang, and Larry L. Stowe

Abstract

CLAVR [cloud from AVHRR (Advanced Very High Resolution Radiometer)] is a global cloud dataset under development at NOAA/NESDIS (National Environmental Satellite, Data, and Information Service). Total cloud amount from two experimental cases, 9 July 1986 and 9 February 1990, are intercompared with two independent products, the Air Force Real-Time Nephanalysis (RTNEPH), and the International Satellite Cloud Climatology Project (ISCCP). The ISCCP cloud database is a climate product processed retrospectively some years after the data are collected. Thus, only CLAVR and RTNEPH can satisfy the real-time requirements for numerical weather prediction (NWP) models. Compared with RTNEPH and ISCCP, which only use two channels in daytime retrievals and one at night, CLAVR utilizes all five channels in daytime and three at night from AVHRR data. That gives CLAVR a greater ability to detect certain cloud types, such as thin cirrus and low stratus. Designed to be an operational product, CLAVR is also compared with total cloud forecasts from the National Meteorological Center (NMC) Medium Range Forecast (MRF) Model. The datasets are mapped to the orbits of NOAA polar satellites, such that errors from temporal sampling are minimized. A set of statistical scores, histograms, and maps are used to display the characteristics of the datasets. The results show that the CLAVR data can realistically resolve global cloud distributions. The spatial variation is, however, less than that of RTNEPH and ISCCP, due to current constraints in the CLAVR treatment of partial cloudiness. Results suggest that if the satellite cloud data is available in real time, it can be used to improve the cloud parameterization in numerical forecast models and data assimilation systems.

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Zhihua He, Long Yang, Fuqiang Tian, Guangheng Ni, Aizhong Hou, and Hui Lu

Abstract

The aim of this study is to evaluate the accuracy of daily rainfall estimates based on the GPM level-3 final product derived from the IMERG algorithm (abbreviated as IMERG) and TRMM 3B42, version 7 (abbreviated as 3B42), in the upper Mekong River basin, a mountainous region in southwestern China. High-density rain gauges provide exceptional resources for ground validation of satellite rainfall estimates over this region. The performance of the two satellite rainfall products is evaluated during two rainy seasons (May–October) over the period 2014–15, as well as their applications in hydrological simulations. Results indicate that 1) IMERG systematically reduces the bias value in rainfall estimates at the gridbox scale and presents a greater ability to capture rainfall variability at the local domain scale compared with 3B42; 2) IMERG improves the ability to capture rain events with moderate intensities and presents higher capability in detecting occurrences of extreme rain events, but significantly overestimates the amounts of these extreme events; and 3) IMERG generally produces comparable daily streamflow simulations to 3B42 and tends to outperform 3B42 in driving hydrological simulations when calibrating model parameters using each rainfall input. This study provides an early evaluation of the IMERG rainfall product over a mountainous region. The findings indicate the potential of the IMERG product in overestimating extreme rain events, which could serve as the basis for further improvement of IMERG rainfall retrieval algorithms. The hydrological evaluations described here could shed light on the emerging application of retrospectively generated IMERG products back to the TRMM era.

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Shi-Keng Yang, Yu-Tai Hou, Alvin J. Miller, and Kenneth A. Campana

Abstract

This study presents an evaluation of the NCEP–NCAR Reanalysis (the reanalysis) by comparing its components of the earth radiation budget to satellite data. Monthly mean clear sky (CS) and total sky of outgoing longwave radiation (OLR), as well as reflected solar radiation (RSW) for 1985 and 1986, are compared to the top-of-the-atmosphere (TOA) measurements from the Earth Radiation Budget Experiment (ERBE). The ERBE-derived data of Staylor and Wilbur are also utilized to validate surface albedo. There are two objectives to this study: (i) to document the general quality of the reanalysis radiation budget, and (ii) to identify some of the general problem areas in the reanalysis global data assimilation system (GDAS).

The OLR comparisons show that the global annual mean from the reanalysis is approximately 1.5% higher than that of ERBE. The zonal-average differences are strongly seasonal, which is particularly evident at high latitudes for the CS OLR, and at most latitudes for total-sky OLR. For the geographical distribution, the synoptic patterns from the reanalysis are in good agreement with the observations. Yet many regions in the Tropics and subtropics pose significant systematic biases. Possible causes are from shortcomings in the the cloud/moisture parameterizations of the reanalysis GDAS. The complex topography unresolvable by the T62 model could also be the cause for the biases in tall mountain regions.

The global RSW comparisons show that the CS data from the reanalysis is in very good agreement with ERBE, while the total-sky RSW data overestimate ERBE by 12.6 W m−2 (∼10%) globally. Persistent overestimates of RSW throughout the period indicate that the global energy budget for the reanalysis is not balanced. This result also is consistent with the finding in OLR suggesting that the reanalysis GDAS contains shortcomings in the cloud/moisture parameterizations. Another possibility for the difference in RSW is deficiencies in the GDAS shortwave parameterizations.

Over the Sahara Desert, the reanalysis underestimates RSW, and overestimates OLR, both in the clear-sky and total-sky conditions. Comparison with the Staylor and Wilber ERBE-derived surface albedo suggests that GDAS surface albedo in this region should be increased by up to 0.1 (in albedo units). A comparison with the interannual variations of the satellite data for the boreal summer illustrates that the radiation budget data of the reanalysis contains a realistic climate signal.

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V. Chandrasekar, Arthur Hou, Eric Smith, V. N. Bringi, S. A. Rutledge, E. Gorgucci, W. A. Petersen, and Gail Skofronick Jackson

Dual-polarization weather radars have evolved significantly in the last three decades culminating in operational deployment by the National Weather Service. In addition to operational applications in the weather service, dual-polarization radars have shown significant potential in contributing to the research fields of ground-based remote sensing of rainfall microphysics, the study of precipitation evolution, and hydrometeor classification. Microphysical characterization of precipitation and quantitative precipitation estimation are important applications that are critical in the validation of satellite-borne precipitation measurements and also serve as valuable tools in algorithm development. This paper presents the important role played by dual-polarization radar in validating spaceborne precipitation measurements. Examples of raindrop size distribution retrievals and hydrometeor-type classification are discussed. The quantitative precipitation estimation is a product of direct relevance to spaceborne observations. During the Tropical Rainfall Measuring Mission (TRMM) program substantial advancement was made with ground-based polarization radars collecting unique observations in the tropics, which are noted. The scientific accomplishments of relevance to spaceborne measurements of precipitation are summarized. The potential of dual-polarization radars and opportunities in the era of the global precipitation measurement mission is also discussed.

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Robert J. Zamora, Ellsworth G. Dutton, Michael Trainer, Stuart A. McKeen, James M. Wilczak, and Yu-Tai Hou

Abstract

In this paper, solar irradiance forecasts made by mesoscale numerical weather prediction models are compared with observations taken during three air-quality experiments in various parts of the United States. The authors evaluated the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5) and the National Centers for Environmental Prediction (NCEP) Eta Model. The observations were taken during the 2000 Texas Air Quality Experiment (TexAQS), the 2000 Central California Ozone Study (CCOS), and the New England Air Quality Study (NEAQS) 2002. The accuracy of the model forecast irradiances show a strong dependence on the aerosol optical depth. Model errors on the order of 100 W m−2 are possible when the aerosol optical depth exceeds 0.1. For smaller aerosol optical depths, the climatological attenuation used in the models yields solar irradiance estimates that are in good agreement with the observations.

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S. Kaspari, P. A. Mayewski, M. Handley, S. Kang, S. Hou, S. Sneed, K. Maasch, and D. Qin

Abstract

A Mount Everest ice core analyzed at high resolution for major and trace elements (Sr, Cs, Ba, La, Ce, Pr, Nd, Sm, Eu, Tb, Dy, Ho, Er, Tm, Yb, Lu, Bi, U, Tl, Al, S, Ca, Ti, V, Cr, Mn, Fe, Co) and spanning the period a.d. 1650–2002 is used to investigate the sources of and variations in atmospheric dust through time. The chemical composition of dust varies seasonally, and peak dust concentrations occur during the winter–spring months. Significant correlations between the Everest dust record and dust observations at stations suggest that the Everest record is representative of regional variations in atmospheric dust loading. Back-trajectory analysis in addition to a significant correlation of Everest dust concentrations and the Total Ozone Mapping Spectrometer (TOMS) aerosol index indicates that the dominant winter sources of dust are the Arabian Peninsula, Thar Desert, and northern Sahara. Factors that contribute to dust generation at the surface include soil moisture and temperature, and the long-range transport of dust aerosols appears to be sensitive to the strength of 500-mb zonal winds. There are periods of high dust concentration throughout the 350-yr Mount Everest dust record; however, there is an increase in these periods since the early 1800s. The record was examined for recent increases in dust emissions associated with anthropogenic activities, but no recent dust variations can be conclusively attributed to anthropogenic inputs of dust.

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Takamichi Iguchi, Toshihisa Matsui, Wei-Kuo Tao, Alexander P. Khain, Vaughan T. J. Phillips, Chris Kidd, Tristan L’Ecuyer, Scott A. Braun, and Arthur Hou

Abstract

Two mixed-phase precipitation events were observed on 21 September and 20 October 2010 over the southern part of Finland during the Light Precipitation Validation Experiment (LPVEx). These events have been simulated using the Weather Research and Forecasting Model coupled with spectral bin microphysics (WRF–SBM). The detailed ice-melting scheme with prognosis of the liquid water fraction during melting enables explicit simulation of microphysical properties in the melting layer. First, the simulations have been compared with C-band 3D radar measurements for the purpose of evaluating the overall profiles of cloud and precipitation. The simulation has some artificial convective patterns and errors in the forecast displacement of the precipitation system. The overall overestimation of reflectivity is consistent with a bias toward the range characterized by large-diameter droplets in the surface drop size distribution. Second, the structure of the melting bands has been evaluated against vertically pointing K-band radar measurements. A peak in reflectivity and a gradual change in Doppler velocity are observed and similarly simulated in the common temperature range from approximately 0° to 3°C. The effectiveness of the time-dependent melting scheme has been justified by intercomparison with a corresponding simulation using an instantaneous melting scheme. A weakness of the new melting scheme is that melting particles having high liquid water fractions on the order of 80%–90% cannot be simulated. This situation may cause underestimation of radar reflectivity in the melting layer because of the assumptions of melting-particle structure used to calculate the scattering properties.

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Arthur Y. Hou, Ramesh K. Kakar, Steven Neeck, Ardeshir A. Azarbarzin, Christian D. Kummerow, Masahiro Kojima, Riko Oki, Kenji Nakamura, and Toshio Iguchi

Precipitation affects many aspects of our everyday life. It is the primary source of freshwater and has significant socioeconomic impacts resulting from natural hazards such as hurricanes, floods, droughts, and landslides. Fundamentally, precipitation is a critical component of the global water and energy cycle that governs the weather, climate, and ecological systems. Accurate and timely knowledge of when, where, and how much it rains or snows is essential for understanding how the Earth system functions and for improving the prediction of weather, climate, freshwater resources, and natural hazard events.

The Global Precipitation Measurement (GPM) mission is an international satellite mission specifically designed to set a new standard for the measurement of precipitation from space and to provide a new generation of global rainfall and snowfall observations in all parts of the world every 3 h. The National Aeronautics and Space Administration (NASA) and the Japan Aerospace and Exploration Agency (JAXA) successfully launched the Core Observatory satellite on 28 February 2014 carrying advanced radar and radiometer systems to serve as a precipitation physics observatory. This will serve as a transfer standard for improving the accuracy and consistency of precipitation measurements from a constellation of research and operational satellites provided by a consortium of international partners. GPM will provide key measurements for understanding the global water and energy cycle in a changing climate as well as timely information useful for a range of regional and global societal applications such as numerical weather prediction, natural hazard monitoring, freshwater resource management, and crop forecasting.

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