Search Results

You are looking at 31 - 40 of 185 items for

  • Author or Editor: S. Wang x
  • Refine by Access: All Content x
Clear All Modify Search
S. Wang, G. H. Huang, B. W. Baetz, and W. Huang

Abstract

This paper presents a factorial possibilistic–probabilistic inference (FPI) framework for estimation of hydrologic parameters and characterization of interactive uncertainties. FPI is capable of incorporating expert knowledge into the parameter adjustment procedure for enhancing the understanding of the nature of the calibration problem. As a component of the FPI framework, a Monte Carlo–based fractional fuzzy–factorial analysis (MFA) method is also proposed to identify the best parameter set and its underlying probability distributions in a fuzzy probability space. Factorial analysis of variance (ANOVA) coupled with its multivariate extensions are performed to explore potential interactions among model parameters and among hydrological metrics in a systematic manner. The proposed methodology is applied to the Xiangxi River watershed by using the conceptual hydrological model (HYMOD) to demonstrate its validity and applicability. Results reveal that MFA is capable of deriving probability density functions (PDFs) of hydrologic model parameters. Moreover, the sequential inferences derived from the F test and its multivariate approximations disclose the statistical significance of parametric interactions affecting individual and multiple hydrological metrics, respectively. The findings presented here indicate that parametric interactions are complex in a fuzzy stochastic environment, and the magnitude and direction of interaction effects vary in different regions of the parameter space as well as vary temporally because of the dynamic behavior of hydrologic systems.

Full access
Yun Hang, Tristan S. L’Ecuyer, David S. Henderson, Alexander V. Matus, and Zhien Wang

Abstract

The role of clouds in modulating vertically integrated atmospheric heating is investigated using CloudSat’s multisensor radiative flux dataset. On the global mean, clouds are found to induce a net atmospheric heating of 0.07 ± 0.08 K day−1 that derives largely from 0.06 ± 0.07 K day−1 of enhanced shortwave absorption and a small, 0.01 ± 0.04 K day−1 reduction of longwave cooling. However, this small global average longwave effect results from the near cancellation of much larger regional warming by multilayered cloud systems in the tropics and cooling from stratocumulus clouds in subtropical oceans. Clouds are observed to warm the tropical atmosphere by 0.23 K day−1 and cool the polar atmosphere by −0.13 K day−1 enhancing required zonal heat redistribution by the meridional overturning circulation. Zonal asymmetries in the occurrence of multilayered clouds that are more frequent in the Northern Hemisphere and stratocumulus that occur more frequently over the southern oceans also leads to 3 times as much cloud heating in the Northern Hemisphere (0.1 K day−1) than the Southern Hemisphere (0.04 K day−1). These findings suggest that clouds very likely make the strongest contribution to the annual mean atmospheric energy imbalance between the hemispheres (2.0 ± 3.5 PW).

Full access
R. D. Koster, S. D. Schubert, H. Wang, S. P. Mahanama, and Anthony M. DeAngelis

Abstract

Flash droughts—uncharacteristically rapid dryings of the land system—are naturally associated with extreme precipitation deficits. Such precipitation deficits, however, do not tell the whole story, for land surface drying can be exacerbated by anomalously high evapotranspiration (ET) rates driven by anomalously high temperatures (e.g., during heat waves), anomalously high incoming radiation (e.g., from reduced cloudiness), and other meteorological anomalies. In this study, the relative contributions of precipitation and ET anomalies to flash drought generation in the Northern Hemisphere are quantified through the analysis of diagnostic fields contained within the MERRA-2 reanalysis product. Unique to the approach is the explicit treatment of soil moisture impacts on ET through relationships diagnosed from the reanalysis data; under this treatment, an ET anomaly that is negative relative to the local long-term climatological mean is still considered positive in terms of its contribution to a flash drought if it is high for the concurrent value of soil moisture. Maps produced in the analysis show the fraction of flash drought production stemming specifically from ET anomalies and illustrate how ET anomalies for some droughts are related to temperature and radiation anomalies. While ET is found to have an important impact on flash drought production in the central United States and in parts of Russia known from past studies to be prone to heat wave–related drought, and while this impact does appear stronger during the onset (first several days) of flash droughts, overall the contribution of ET to these droughts is small relative to the contribution of precipitation deficit.

Full access
R. Cifelli, V. Chandrasekar, S. Lim, P. C. Kennedy, Y. Wang, and S. A. Rutledge

Abstract

The efficacy of dual-polarization radar for quantitative precipitation estimation (QPE) has been demonstrated in a number of previous studies. Specifically, rainfall retrievals using combinations of reflectivity (Z h), differential reflectivity (Z dr), and specific differential phase (K dp) have advantages over traditional ZR methods because more information about the drop size distribution (DSD) and hydrometeor type are available. In addition, dual-polarization-based rain-rate estimators can better account for the presence of ice in the sampling volume.

An important issue in dual-polarization rainfall estimation is determining which method to employ for a given set of polarimetric observables. For example, under what circumstances does differential phase information provide superior rain estimates relative to methods using reflectivity and differential reflectivity? At Colorado State University (CSU), an optimization algorithm has been developed and used for a number of years to estimate rainfall based on thresholds of Z h, Z dr, and K dp. Although the algorithm has demonstrated robust performance in both tropical and midlatitude environments, results have shown that the retrieval is sensitive to the selection of the fixed thresholds.

In this study, a new rainfall algorithm is developed using hydrometeor identification (HID) to guide the choice of the particular rainfall estimation algorithm. A separate HID algorithm has been developed primarily to guide the rainfall application with the hydrometeor classes, namely, all rain, mixed precipitation, and all ice.

Both the data collected from the S-band Colorado State University–University of Chicago–Illinois State Water Survey (CSU–CHILL) radar and a network of rain gauges are used to evaluate the performance of the new algorithm in mixed rain and hail in Colorado. The evaluation is also performed using an algorithm similar to the one developed for the Joint Polarization Experiment (JPOLE). Results show that the new CSU HID-based algorithm provides good performance for the Colorado case studies presented here.

Full access
L. Peng, S.-T. Wang, S.-L. Shieh, M.-D. Cheng, and T.-C. Yeh

Abstract

Surface tracks of some cross-Taiwan tropical cyclones were discontinuous as a result of the blockage of the north-northeast–south-southwest-oriented Central Mountain Range (CMR). This paper tries to identify the variables that may be used to diagnose track continuity in advance. The track records of 131 westbound cross-Taiwan tropical cyclones between 1897 and 2009 are examined. It is found that the track continuity of a westbound cross-Taiwan tropical cyclone depends mostly upon the landfall location (YLF), the approaching direction (ANG), and the maximum wind (VMX) of the cyclone. According to the empirical probability of track continuity estimated from the data, the dependence on YLF, which is nonlinear and remarkably asymmetric with respect to the midpoint of the east coast, may be well approximated by a quadratic function of YLF. The nonlinearity and asymmetry can be interpreted in terms of the length scale of the CMR and the north–south antisymmetry of the cyclonic flow. The estimated dependence of track continuity on cyclone intensity and size may be approximated by a linear function of VMX. The estimated dependence of track continuity on ANG may be approximated by a single term of the modified variable DIR (=|ANG − 110|, where 110 is the direction, in degrees, perpendicular to the CMR’s long axis).

Using the 64 tracks between 1944 and 1996 as the training sample, a logistic regression equation model, built in terms of YLF, YLF square, DIR, and VMX gives an overall accuracy score of 89%. As to the probability estimates of individual tracks, 49 of the 64 tracks have estimated probabilities outside the (0.5 − 0.127, 0.5 + 0.127) RMS error range and are correctly classified. A prediction test using another set of 67 tracks not included in the model-training sample, scores a success rate of 82%. As to the probability predictions for individual tracks, 49 of the 67 tracks have predicted probabilities outside the RMS error range and are correctly predicted. These results confirm the appropriateness of the model and moreover demonstrate that the three parameters, YLF, DIR, and VMX, primarily control the surface track continuity of a westbound tropical cyclone crossing Taiwan.

Full access
Laura C. Slivinski, Gilbert P. Compo, Jeffrey S. Whitaker, Prashant D. Sardeshmukh, Jih-Wang A. Wang, Kate Friedman, and Chesley McColl

Abstract

Given the network of satellite and aircraft observations around the globe, do additional in situ observations impact analyses within a global forecast system? Despite the dense observational network at many levels in the tropical troposphere, assimilating additional sounding observations taken in the eastern tropical Pacific Ocean during the 2016 El Niño Rapid Response (ENRR) locally improves wind, temperature, and humidity 6-h forecasts using a modern assimilation system. Fields from a 50-km reanalysis that assimilates all available observations, including those taken during the ENRR, are compared with those from an otherwise-identical reanalysis that denies all ENRR observations. These observations reveal a bias in the 200-hPa divergence of the assimilating model during a strong El Niño. While the existing observational network partially corrects this bias, the ENRR observations provide a stronger mean correction in the analysis. Significant improvements in the mean-square fit of the first-guess fields to the assimilated ENRR observations demonstrate that they are valuable within the existing network. The effects of the ENRR observations are pronounced in levels of the troposphere that are sparsely observed, particularly 500–800 hPa. Assimilating ENRR observations has mixed effects on the mean-square difference with nearby non-ENRR observations. Using a similar system but with a higher-resolution forecast model yields comparable results to the lower-resolution system. These findings imply a limited improvement in large-scale forecast variability from additional in situ observations, but significant improvements in local 6-h forecasts.

Full access
R. A. Hansell, S. C. Tsay, Q. Ji, N. C. Hsu, M. J. Jeong, S. H. Wang, J. S. Reid, K. N. Liou, and S. C. Ou

Abstract

In September 2006, NASA Goddard’s mobile ground-based laboratories were deployed to Sal Island in Cape Verde (16.73°N, 22.93°W) to support the NASA African Monsoon Multidisciplinary Analysis (NAMMA) field study. The Atmospheric Emitted Radiance Interferometer (AERI), a key instrument for spectrally characterizing the thermal IR, was used to retrieve the dust IR aerosol optical depths (AOTs) in order to examine the diurnal variability of airborne dust with emphasis on three separate dust events. AERI retrievals of dust AOT are compared with those from the coincident/collocated multifilter rotating shadowband radiometer (MFRSR), micropulse lidar (MPL), and NASA Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) sensors. The retrieved AOTs are then inputted into the Fu–Liou 1D radiative transfer model to evaluate local instantaneous direct longwave radiative effects (DRELW) of dust at the surface in cloud-free atmospheres and its sensitivity to dust microphysical parameters. The top-of-atmosphere DRELW and longwave heating rate profiles are also evaluated. Instantaneous surface DRELW ranges from 2 to 10 W m−2 and exhibits a strong linear dependence with dust AOT yielding a DRELW of 16 W m−2 per unit dust AOT. The DRELW is estimated to be ∼42% of the diurnally averaged direct shortwave radiative effect at the surface but of opposite sign, partly compensating for the shortwave losses. Certainly nonnegligible, the authors conclude that DRELW can significantly impact the atmospheric energetics, representing an important component in the study of regional climate variation.

Full access
S. Saha, S. Nadiga, C. Thiaw, J. Wang, W. Wang, Q. Zhang, H. M. Van den Dool, H.-L. Pan, S. Moorthi, D. Behringer, D. Stokes, M. Peña, S. Lord, G. White, W. Ebisuzaki, P. Peng, and P. Xie

Abstract

The Climate Forecast System (CFS), the fully coupled ocean–land–atmosphere dynamical seasonal prediction system, which became operational at NCEP in August 2004, is described and evaluated in this paper. The CFS provides important advances in operational seasonal prediction on a number of fronts. For the first time in the history of U.S. operational seasonal prediction, a dynamical modeling system has demonstrated a level of skill in forecasting U.S. surface temperature and precipitation that is comparable to the skill of the statistical methods used by the NCEP Climate Prediction Center (CPC). This represents a significant improvement over the previous dynamical modeling system used at NCEP. Furthermore, the skill provided by the CFS spatially and temporally complements the skill provided by the statistical tools. The availability of a dynamical modeling tool with demonstrated skill should result in overall improvement in the operational seasonal forecasts produced by CPC.

The atmospheric component of the CFS is a lower-resolution version of the Global Forecast System (GFS) that was the operational global weather prediction model at NCEP during 2003. The ocean component is the GFDL Modular Ocean Model version 3 (MOM3). There are several important improvements inherent in the new CFS relative to the previous dynamical forecast system. These include (i) the atmosphere–ocean coupling spans almost all of the globe (as opposed to the tropical Pacific only); (ii) the CFS is a fully coupled modeling system with no flux correction (as opposed to the previous uncoupled “tier-2” system, which employed multiple bias and flux corrections); and (iii) a set of fully coupled retrospective forecasts covering a 24-yr period (1981–2004), with 15 forecasts per calendar month out to nine months into the future, have been produced with the CFS.

These 24 years of fully coupled retrospective forecasts are of paramount importance to the proper calibration (bias correction) of subsequent operational seasonal forecasts. They provide a meaningful a priori estimate of model skill that is critical in determining the utility of the real-time dynamical forecast in the operational framework. The retrospective dataset also provides a wealth of information for researchers to study interactive atmosphere–land–ocean processes.

Full access
Kefeng Zhu, Yujie Pan, Ming Xue, Xuguang Wang, Jeffrey S. Whitaker, Stanley G. Benjamin, Stephen S. Weygandt, and Ming Hu

Abstract

A regional ensemble Kalman filter (EnKF) system is established for potential Rapid Refresh (RAP) operational application. The system borrows data processing and observation operators from the gridpoint statistical interpolation (GSI), and precalculates observation priors using the GSI. The ensemble square root Kalman filter (EnSRF) algorithm is used, which updates both the state vector and observation priors. All conventional observations that are used in the operational RAP GSI are assimilated. To minimize computational costs, the EnKF is run at ⅓ of the operational RAP resolution or about 40-km grid spacing, and its performance is compared to the GSI using the same datasets and resolution. Short-range (up to 18 h, the RAP forecast length) forecasts are verified against soundings, surface observations, and precipitation data. Experiments are run with 3-hourly assimilation cycles over a 9-day convectively active retrospective period from spring 2010. The EnKF performance was improved by extensive tuning, including the use of height-dependent covariance localization scales and adaptive covariance inflation. When multiple physics parameterization schemes are employed by the EnKF, forecast errors are further reduced, especially for relative humidity and temperature at the upper levels and for surface variables. The best EnKF configuration produces lower forecast errors than the parallel GSI run. Gilbert skill scores of precipitation forecasts on the 13-km RAP grid initialized from the 3-hourly EnKF analyses are consistently better than those from GSI analyses.

Full access
Yujie Pan, Kefeng Zhu, Ming Xue, Xuguang Wang, Ming Hu, Stanley G. Benjamin, Stephen S. Weygandt, and Jeffrey S. Whitaker

Abstract

A coupled ensemble square root filter–three-dimensional ensemble-variational hybrid (EnSRF–En3DVar) data assimilation (DA) system is developed for the operational Rapid Refresh (RAP) forecasting system. The En3DVar hybrid system employs the extended control variable method, and is built on the NCEP operational gridpoint statistical interpolation (GSI) three-dimensional variational data assimilation (3DVar) framework. It is coupled with an EnSRF system for RAP, which provides ensemble perturbations. Recursive filters (RF) are used to localize ensemble covariance in both horizontal and vertical within the En3DVar. The coupled En3DVar hybrid system is evaluated with 3-h cycles over a 9-day period with active convection. All conventional observations used by operational RAP are included. The En3DVar hybrid system is run at ⅓ of the operational RAP horizontal resolution or about 40-km grid spacing, and its performance is compared to parallel GSI 3DVar and EnSRF runs using the same datasets and resolution. Short-term forecasts initialized from the 3-hourly analyses are verified against sounding and surface observations. When using equally weighted static and ensemble background error covariances and 40 ensemble members, the En3DVar hybrid system outperforms the corresponding GSI 3DVar and EnSRF. When the recursive filter coefficients are tuned to achieve a similar height-dependent localization as in the EnSRF, the En3DVar results using pure ensemble covariance are close to EnSRF. Two-way coupling between EnSRF and En3DVar did not produce noticeable improvement over one-way coupling. Downscaled precipitation forecast skill on the 13-km RAP grid from the En3DVar hybrid is better than those from GSI 3DVar analyses.

Full access