Search Results

You are looking at 1 - 9 of 9 items for

  • Author or Editor: Yu-Tai Hou x
  • Refine by Access: All Content x
Clear All Modify Search
Owen E. Thompson and Yu-Tai Hou

Abstract

Satellite sensor optical systems now provide scan spots that are reasonably small and closely spaced compared with the horizontal scales of atmospheric variability that meteorologists might like to infer. Furthermore, geo-synchronous deployment of meteorological satellites provides the opportunity for almost arbitrarily frequent observational thus apparently increasing the temporal resolving power to almost an arbitrary degree. It is known, however, that the vertical resolving power of satellite sounding technology is poor compared with RAOBs. That is, finer scales of vertical atmospheric structure that can he resolved by radiosonde observations may not be detectable or resolvable by satellite radiance observations. In all retrieval methods, some external information or estimates of the ambient vertical structure must be included so that the apparent vertical resolution of the retrieval system is increased.

In this study we show that horizontal and temporal resolving power of satellite soundings is closely coupled to the vertical resolving power. This is demonstrated both empirically and theoretically. Moreover, we show that improving the horizontal or temporal resolving power is not a simple matter of improving scan spot optics or increasing the frequency of observation. Either the “vertical resolving power” of the satellite instrument itself must be improved, or the a priori external information must be extended to include correct horizontal and temporal structure. The vertical resolution limitation of satellite sounders has a direct effect on filtering out, or distorting, horizontal and temporal structural information.

Full access
Owen E. Thompson, Donald D. Dazlich, and Yu-Tai Hou

Abstract

The inverse problem of satellite temperature profile retrieval is well known to be ill-posed. Ibis means that not only is a vertical temperature profile solution not unique, but that two solutions can be very different from each other. A set of atmosphere-like, and true atmospheric examples of significantly dissimilar inverse solutions, were sought and found, using an 11-channel simulated HIRS sounding radiometer. Using the Riemann-Lebesgue Lemma for guidance, it is shown that simultaneous, numerical solutions of an atmospheric character may differ by as much as 10 K between 10–1000 mb. However, an empirical search for dissimilar solutions in the natural atmosphere reveals an extremely low probability of finding two significantly different RAOBs which produce radiance measurements whose differences cannot be resolved by the satellite radiometer. The empirical results are used to derive a first estimate of the limits of retrievability, analogous to the limits of predictability derivable from the ill-posed nature of the numerical weather prediction problem.

Full access
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.

Full access
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.

Full access
Changyong Cao, Hui Xu, Jerry Sullivan, Larry McMillin, Pubu Ciren, and Yu-Tai Hou

Abstract

Intersatellite radiance comparisons for the 19 infrared channels of the High-Resolution Infrared Radiation Sounders (HIRS) on board NOAA-15, -16, and -17 are performed with simultaneous nadir observations at the orbital intersections of the satellites in the polar regions, where each pair of the HIRS views the same earth target within a few seconds. Analysis of such datasets from 2000 to 2003 reveals unambiguous intersatellite radiance differences as well as calibration anomalies.

The results show that in general, the intersatellite relative biases are less than 0.5 K for most HIRS channels. The large biases in different channels differ in both magnitude and sign, and are likely to be caused by the differences and measurement uncertainties in the HIRS spectral response functions. The seasonal bias variation in the stratosphere channels is found to be highly correlated with the lapse rate factor approximated by the channel radiance differences. The method presented in this study works particularly well for channels sensing the stratosphere because of the relative spatial uniformity and stability of the stratosphere, for which the intercalibration accuracy and precision are mostly limited by the instrument noise. This method is simple and robust, and the results are highly repeatable and unambiguous. Intersatellite radiance calibration with this method is very useful for the on-orbit verification and monitoring of instrument performance, and is potentially useful for constructing long-term time series for climate studies.

Full access
Fanglin Yang, Kenneth Mitchell, Yu-Tai Hou, Yongjiu Dai, Xubin Zeng, Zhuo Wang, and Xin-Zhong Liang

Abstract

This study examines the dependence of surface albedo on solar zenith angle (SZA) over snow-free land surfaces using the intensive observations of surface shortwave fluxes made by the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) Program and the National Oceanic and Atmospheric Administration Surface Radiation Budget Network (SURFRAD) in 1997–2005. Results are used to evaluate the National Centers for Environmental Prediction (NCEP) Global Forecast Systems (GFS) parameterization and several new parameterizations derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) products. The influence of clouds on surface albedo and the albedo difference between morning and afternoon observations are also investigated. A new approach is taken to partition the observed upward flux so that the direct-beam and diffuse albedos can be separately computed. The study focused first on the ARM Southern Great Plains Central Facility site. It is found that the diffuse albedo prescribed in the NCEP GFS matched closely with the observations. The direct-beam albedo parameterized in the GFS is largely underestimated at all SZAs. The parameterizations derived from the MODIS product underestimated the direct-beam albedo at large SZAs and slightly overestimated it at small SZAs. Similar results are obtained from the analyses of observations at other stations. It is also found that the morning and afternoon dependencies of direct-beam albedo on SZA differ among the stations. Attempts are made to improve numerical model algorithms that parameterize the direct-beam albedo as a product of the direct-beam albedo at SZA = 60° (or the diffuse albedo), which varies with surface type or geographical location and/or season, and a function that depends only on SZA. A method is presented for computing the direct-beam albedos over these snow-free land points without referring to a particular land-cover classification scheme, which often differs from model to model.

Full access
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.

Full access
Suranjana Saha, Shrinivas Moorthi, Xingren Wu, Jiande Wang, Sudhir Nadiga, Patrick Tripp, David Behringer, Yu-Tai Hou, Hui-ya Chuang, Mark Iredell, Michael Ek, Jesse Meng, Rongqian Yang, Malaquías Peña Mendez, Huug van den Dool, Qin Zhang, Wanqiu Wang, Mingyue Chen, and Emily Becker

Abstract

The second version of the NCEP Climate Forecast System (CFSv2) was made operational at NCEP in March 2011. This version has upgrades to nearly all aspects of the data assimilation and forecast model components of the system. A coupled reanalysis was made over a 32-yr period (1979–2010), which provided the initial conditions to carry out a comprehensive reforecast over 29 years (1982–2010). This was done to obtain consistent and stable calibrations, as well as skill estimates for the operational subseasonal and seasonal predictions at NCEP with CFSv2. The operational implementation of the full system ensures a continuity of the climate record and provides a valuable up-to-date dataset to study many aspects of predictability on the seasonal and subseasonal scales. Evaluation of the reforecasts show that the CFSv2 increases the length of skillful MJO forecasts from 6 to 17 days (dramatically improving subseasonal forecasts), nearly doubles the skill of seasonal forecasts of 2-m temperatures over the United States, and significantly improves global SST forecasts over its predecessor. The CFSv2 not only provides greatly improved guidance at these time scales but also creates many more products for subseasonal and seasonal forecasting with an extensive set of retrospective forecasts for users to calibrate their forecast products. These retrospective and real-time operational forecasts will be used by a wide community of users in their decision making processes in areas such as water management for rivers and agriculture, transportation, energy use by utilities, wind and other sustainable energy, and seasonal prediction of the hurricane season.

Full access
Suranjana Saha, Shrinivas Moorthi, Hua-Lu Pan, Xingren Wu, Jiande Wang, Sudhir Nadiga, Patrick Tripp, Robert Kistler, John Woollen, David Behringer, Haixia Liu, Diane Stokes, Robert Grumbine, George Gayno, Jun Wang, Yu-Tai Hou, Hui-ya Chuang, Hann-Ming H. Juang, Joe Sela, Mark Iredell, Russ Treadon, Daryl Kleist, Paul Van Delst, Dennis Keyser, John Derber, Michael Ek, Jesse Meng, Helin Wei, Rongqian Yang, Stephen Lord, Huug van den Dool, Arun Kumar, Wanqiu Wang, Craig Long, Muthuvel Chelliah, Yan Xue, Boyin Huang, Jae-Kyung Schemm, Wesley Ebisuzaki, Roger Lin, Pingping Xie, Mingyue Chen, Shuntai Zhou, Wayne Higgins, Cheng-Zhi Zou, Quanhua Liu, Yong Chen, Yong Han, Lidia Cucurull, Richard W. Reynolds, Glenn Rutledge, and Mitch Goldberg

The NCEP Climate Forecast System Reanalysis (CFSR) was completed for the 31-yr period from 1979 to 2009, in January 2010. The CFSR was designed and executed as a global, high-resolution coupled atmosphere–ocean–land surface–sea ice system to provide the best estimate of the state of these coupled domains over this period. The current CFSR will be extended as an operational, real-time product into the future. New features of the CFSR include 1) coupling of the atmosphere and ocean during the generation of the 6-h guess field, 2) an interactive sea ice model, and 3) assimilation of satellite radiances by the Gridpoint Statistical Interpolation (GSI) scheme over the entire period. The CFSR global atmosphere resolution is ~38 km (T382) with 64 levels extending from the surface to 0.26 hPa. The global ocean's latitudinal spacing is 0.25° at the equator, extending to a global 0.5° beyond the tropics, with 40 levels to a depth of 4737 m. The global land surface model has four soil levels and the global sea ice model has three layers. The CFSR atmospheric model has observed variations in carbon dioxide (CO2) over the 1979–2009 period, together with changes in aerosols and other trace gases and solar variations. Most available in situ and satellite observations were included in the CFSR. Satellite observations were used in radiance form, rather than retrieved values, and were bias corrected with “spin up” runs at full resolution, taking into account variable CO2 concentrations. This procedure enabled the smooth transitions of the climate record resulting from evolutionary changes in the satellite observing system.

CFSR atmospheric, oceanic, and land surface output products are available at an hourly time resolution and a horizontal resolution of 0.5° latitude × 0.5° longitude. The CFSR data will be distributed by the National Climatic Data Center (NCDC) and NCAR. This reanalysis will serve many purposes, including providing the basis for most of the NCEP Climate Prediction Center's operational climate products by defining the mean states of the atmosphere, ocean, land surface, and sea ice over the next 30-yr climate normal (1981–2010); providing initial conditions for historical forecasts that are required to calibrate operational NCEP climate forecasts (from week 2 to 9 months); and providing estimates and diagnoses of the Earth's climate state over the satellite data period for community climate research.

Preliminary analysis of the CFSR output indicates a product that is far superior in most respects to the reanalysis of the mid-1990s. The previous NCEP–NCAR reanalyses have been among the most used NCEP products in history; there is every reason to believe the CFSR will supersede these older products both in scope and quality, because it is higher in time and space resolution, covers the atmosphere, ocean, sea ice, and land, and was executed in a coupled mode with a more modern data assimilation system and forecast model.

Full access