Search Results

You are looking at 1 - 8 of 8 items for

  • Author or Editor: Chung-Lin Shie x
  • All content x
Clear All Modify Search
Jason A. Sippel, Scott A. Braun, and Chung-Lin Shie

Abstract

This study uses mesoscale ensemble forecasts to compare the magnitude of nonaerosol effects of the Saharan air layer (SAL) with other environmental influences on the intensity of Tropical Storm Debby. Debby was a weak Cape Verde storm that dissipated over the tropical North Atlantic a few days after forming in August 2006. The system has received considerable attention because of its vicinity to the SAL as it struggled to intensify, which has led to speculation that the SAL helped lead to the storm’s demise. Statistical correlation is used to better understand why some ensemble members strengthen the pre-Debby wave into a hurricane and others develop only a weak vortex.

Although the results here suggest that the SAL slowed intensification during the predepression to depression stages, it was not likely responsible for Debby’s dissipation. The most obvious SAL-related factor to affect long-term intensity in the ensembles is dry air above 2 km, which delays organization of the low-level vortex. Warm temperatures within the SAL and shear associated with the African easterly jet (AEJ) exhibit a weak, secondary relationship with forecast intensity variability. An important result here is that sensitivity to the dry environmental air depends considerably on cyclone strength, and it becomes insignificant once a tropical storm forms. Furthermore, Debby’s most rapid period of intensification coincided with its track over somewhat higher sea surface temperatures, and intensification ended when the storm moved over cooler waters. The results herein suggest that this factor might have affected the storm’s intensity more strongly than did any effect of the SAL. Even later, subsequent to the period examined by these ensembles, Debby dissipated under the influence of stronger vertical wind shear from an upper-level trough.

These results show that the relationship among the SAL, AEJ, and developing tropical cyclones is not as straightforward as has been hypothesized by some recent studies. Ultimately, the nuanced relationship between storm intensity and the SAL shows that much care needs to be taken before drawing conclusions about the effect of the SAL on any particular cyclone. The authors therefore advocate more rigorous future analysis through both idealized and ensemble studies to more fully quantify the effect of the SAL on tropical cyclones in general.

Full access
Shu-Hsien Chou, Robert M. Atlas, Chung-Lin Shie, and Joe Ardizzone

Abstract

Monthly averages of daily latent heat fluxes over the oceans for February and August 1988 are estimated using a stability-dependent bulk scheme. Daily fluxes are computed from daily SSM/I (Special Sensor Microwave/Imager) wind speeds and EOF-retrieved SSM/I surface humidity, National Meteorological Center sea surface temperatures, and the European Centre for Medium-Range Weather Forecasts analyzed 2-m temperatures. Daily surface specific humidity (Q) is estimated from SSM/I precipitable water of total (W) and a 500-m bottom layer (W B) using an EOF (empirical orthogonal function) method. This method has six W-based categories of EOFs (independent of geographical locations) and is developed using 23 177 FGGE IIb humidity soundings over the global oceans. For 1200 FGGE IIb humidity soundings, the accuracy of EOF-retrieved Q is 0.75 g kg−1 for the case without errors in W and W B, and increases to 1.16 g kg−1 for the case with errors in W and W B. Compared to 342 collocated radiosonde observations, the EOF-retrieved SSM/I Q has an accuracy of 1.7 g kg−1. The method improves upon the humidity retrieval of Liu and is competitive with that of Schulz et al.

The SSM/I surface humidity and latent heat fluxes of these two months agree reasonably well with those of COADS (Comprehensive Ocean–Atmosphere Data Set). Compared to the COADS, the sea–air humidity difference of SSM/I has a positive bias of approximately 1–3 g kg−1 (an overestimation of flux) over the wintertime trade wind belts and wintertime extratropical oceans. In the summertime extratropical Pacific and summertime eastern equatorial Pacific Ocean, it has a negative bias of about 1–2 g kg−1 (an underestimation of flux). The results further suggest that the two monthly flux estimates, computed from daily and monthly mean data, do not differ significantly over the oceans.

Full access
Scott A. Braun, Jason A. Sippel, Chung-Lin Shie, and Ryan A. Boller

Abstract

The Saharan air layer (SAL) has received considerable attention in recent years as a potential negative influence on the formation and development of Atlantic tropical cyclones. Observations of substantial Saharan dust in the near environment of Hurricane Helene (2006) during the National Aeronautics and Space Administration (NASA) African Monsoon Multidisciplinary Activities (AMMA) Experiment (NAMMA) field campaign led to suggestions about the suppressing influence of the SAL in this case. In this study, a suite of satellite remote sensing data, global meteorological analyses, and airborne data are used to characterize the evolution of the SAL in the environment of Helene and assess its possible impact on the intensity of the storm. The influence of the SAL on Helene appears to be limited to the earliest stages of development, although the magnitude of that impact is difficult to determine observationally. Saharan dust was observed on the periphery of the storm during the first two days of development after genesis when intensification was slow. Much of the dust was observed to move well westward of the storm thereafter, with little SAL air present during the remainder of the storm's lifetime and with the storm gradually becoming a category-3 strength storm four days later. Dry air observed to wrap around the periphery of Helene was diagnosed to be primarily non-Saharan in origin (the result of subsidence) and appeared to have little impact on storm intensity. The eventual weakening of the storm is suggested to result from an eyewall replacement cycle and substantial reduction of the sea surface temperatures beneath the hurricane as its forward motion decreased.

Full access
Mircea Grecu, William S. Olson, Chung-Lin Shie, Tristan S. L’Ecuyer, and Wei-Kuo Tao

Abstract

In this study, satellite passive microwave sensor observations from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) are utilized to make estimates of latent + eddy sensible heating rates (Q 1QR) where Q 1 is the apparent heat source and QR is the radiative heating rate in regions of precipitation. The TMI heating algorithm (herein called TRAIN) is calibrated or “trained” using relatively accurate estimates of heating based on spaceborne Precipitation Radar (PR) observations collocated with the TMI observations over a one-month period. The heating estimation technique is based on a previously described Bayesian methodology, but with improvements in supporting cloud-resolving model simulations, an adjustment of precipitation echo tops to compensate for model biases, and a separate scaling of convective and stratiform heating components that leads to an approximate balance between estimated vertically integrated condensation and surface precipitation.

Estimates of Q 1QR from TMI compare favorably with the PR training estimates and show only modest sensitivity to the cloud-resolving model simulations of heating used to construct the training data. Moreover, the net condensation in the corresponding annual mean satellite latent heating profile is within a few percent of the annual mean surface precipitation rate over the tropical and subtropical oceans where the algorithm is applied. Comparisons of Q 1 produced by combining TMI Q 1QR with independently derived estimates of QR show reasonable agreement with rawinsonde-based analyses of Q 1 from two field campaigns, although the satellite estimates exhibit heating profile structures with sharper and more intense heating peaks than the rawinsonde estimates.

Full access
Shu-Hsien Chou, Eric Nelkin, Joe Ardizzone, Robert M. Atlas, and Chung-Lin Shie

Abstract

Information on the turbulent fluxes of momentum, latent heat, and sensible heat at the air–sea interface is essential in improving model simulations of climate variations and in climate studies. A 13.5-yr (July 1987–December 2000) dataset of daily surface turbulent fluxes over global oceans has been derived from the Special Sensor Microwave Imager (SSM/I) radiance measurements. This dataset, Goddard Satellite-based Surface Turbulent Fluxes, version 2 (GSSTF2), has a spatial resolution of 1° × 1° latitude–longitude and a temporal resolution of 1 day. Turbulent fluxes are derived from the SSM/I surface winds and surface air humidity, as well as the 2-m air and sea surface temperatures (SST) of the NCEP–NCAR reanalysis, using a bulk aerodynamic algorithm based on the surface layer similarity theory.

The GSSTF2 bulk flux model is validated by comparing hourly turbulent fluxes computed from ship data using the model with those observed fluxes of 10 field experiments over the tropical and midlatitude oceans during 1991–99. In addition, the GSSTF2 daily wind stress, latent heat flux, wind speed, surface air humidity, and SST compare reasonably well with those of the collocated measurements of the field experiments. The global distributions of 1988–2000 annual- and seasonal-mean turbulent fluxes show reasonable patterns related to the atmospheric general circulation and seasonal variations. Zonal averages of latent heat fluxes and input parameters over global oceans during 1992–93 have been compared among several flux datasets: GSSTF1 (version 1), GSSTF2, the Hamburg Ocean–Atmosphere Parameters and Fluxes from Satellite Data (HOAPS), NCEP–NCAR reanalysis, and one based on the Comprehensive Ocean–Atmosphere Data Set (COADS). Significant differences are found among the five. These analyses suggest that the GSSTF2 latent heat flux, surface air humidity, and winds are likely to be more realistic than the other four flux datasets examined, although those of GSSTF2 are still subject to regional biases. The GSSTF2 is useful for climate studies and has been submitted to the sea surface turbulent flux project (SEAFLUX) for intercomparison studies.

Full access
Shoichi Shige, Yukari N. Takayabu, Wei-Kuo Tao, and Chung-Lin Shie

Abstract

The spectral latent heating (SLH) algorithm was developed for the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) in Part I of this study. The method uses PR information [precipitation-top height (PTH), precipitation rates at the surface and melting level, and rain type] to select heating profiles from lookup tables. Heating-profile lookup tables for the three rain types—convective, shallow stratiform, and anvil rain (deep stratiform with a melting level)—were derived from numerical simulations of tropical cloud systems from the Tropical Ocean and Global Atmosphere Coupled Ocean–Atmosphere Response Experiment (TOGA COARE) utilizing a cloud-resolving model (CRM). To assess its global application to TRMM PR data, the universality of the lookup tables from the TOGA COARE simulations is examined in this paper. Heating profiles are reconstructed from CRM-simulated parameters (i.e., PTH, precipitation rates at the surface and melting level, and rain type) and are compared with the true CRM-simulated heating profiles, which are computed directly by the model thermodynamic equation. CRM-simulated data from the Global Atmospheric Research Program Atlantic Tropical Experiment (GATE), South China Sea Monsoon Experiment (SCSMEX), and Kwajalein Experiment (KWAJEX) are used as a consistency check. The consistency check reveals discrepancies between the SLH-reconstructed and Goddard Cumulus Ensemble (GCE)-simulated heating above the melting level in the convective region and at the melting level in the stratiform region that are attributable to the TOGA COARE table. Discrepancies in the convective region are due to differences in the vertical distribution of deep convective heating due to the relative importance of liquid and ice water processes, which varies from case to case. Discrepancies in the stratiform region are due to differences in the level separating upper-level heating and lower-level cooling. Based on these results, improvements were made to the SLH algorithm. Convective heating retrieval is now separated into upper-level heating due to ice processes and lower-level heating due to liquid water processes. In the stratiform region, the heating profile is shifted up or down by matching the melting level in the TOGA COARE lookup table with the observed one. Consistency checks indicate the revised SLH algorithm performs much better for both the convective and stratiform components than does the original one. The revised SLH algorithm was applied to PR data, and the results were compared with heating profiles derived diagnostically from SCSMEX sounding data. Key features of the vertical profiles agree well—in particular, the level of maximum heating. The revised SLH algorithm was also applied to PR data for February 1998 and February 1999. The results are compared with heating profiles derived by the convective–stratiform heating (CSH) algorithm. Because observed information on precipitation depth is used in addition to precipitation type and intensity, differences between shallow and deep convection are more distinct in the SLH algorithm in comparison with the CSH algorithm.

Full access
Michael A. Brunke, Zhuo Wang, Xubin Zeng, Michael Bosilovich, and Chung-Lin Shie

Abstract

Ocean surface turbulent fluxes play an important role in the energy and water cycles of the atmosphere–ocean coupled system, and several flux products have become available in recent years. Here, turbulent fluxes from 6 widely used reanalyses, 4 satellite-derived flux products, and 2 combined product are evaluated by comparison with direct covariance latent heat (LH) and sensible heat (SH) fluxes and inertial-dissipation wind stresses measured from 12 cruises over the tropics and mid- and high latitudes. The biases range from −3.0 to 20.2 W m−2 for LH flux, from −1.4 to 6.0 W m−2 for SH flux, and from −7.6 to 7.9 × 10−3 N m−2 for wind stress. These biases are small for moderate wind speeds but diverge for strong wind speeds (>10 m s−1). The total flux biases are then further evaluated by dividing them into uncertainties due to errors in the bulk variables and the residual uncertainty. The bulk-variable-caused uncertainty dominates many products’ SH flux and wind stress biases. The biases in the bulk variables that contribute to this uncertainty can be quite high depending on the cruise and the variable. On the basis of a ranking of each product’s flux, it is found that the Modern-Era Retrospective Analysis for Research and Applications (MERRA) is among the “best performing” for all three fluxes. Also, the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim) and the National Centers for Environmental Prediction–Department of Energy (NCEP–DOE) reanalysis are among the best performing for two of the three fluxes. Of the satellite-derived products, version 2b of the Goddard Satellite-Based Surface Turbulent Fluxes (GSSTF2b) is among the best performing for two of the three fluxes. Also among the best performing for only one of the fluxes are the 40-yr ERA (ERA-40) and the combined product objectively analyzed air–sea fluxes (OAFlux). Direction for the future development of ocean surface flux datasets is also suggested.

Full access
Xiping Zeng, Wei-Kuo Tao, Minghua Zhang, Christa Peters-Lidard, Stephen Lang, Joanne Simpson, Sujay Kumar, Shaocheng Xie, Joseph L. Eastman, Chung-Lin Shie, and James V. Geiger

Abstract

Two 20-day, continental midlatitude cases are simulated with a three-dimensional (3D) cloud-resolving model (CRM) and are compared to Atmospheric Radiation Measurement Program (ARM) data. Surface fluxes from ARM ground stations and a land data assimilation system are used to drive the CRM. This modeling evaluation shows that the model simulates precipitation well but overpredicts clouds, especially in the upper troposphere. The evaluation also shows that the ARM surface fluxes can have noticeable errors in summertime.

Theoretical analysis reveals that buoyancy damping is sensitive to spatial smoothers in two-dimensional (2D) CRMs, but not in 3D ones. With this theoretical analysis and the ARM cloud observations as background, 2D and 3D simulations are compared, showing that the 2D CRM has not only rapid fluctuations in surface precipitation but also spurious dehumidification (or a decrease in cloud amount). The present study suggests that the rapid precipitation fluctuation and spurious dehumidification be attributed to the sensitivity of buoyancy damping to dimensionality.

Full access