Search Results

You are looking at 1 - 5 of 5 items for :

  • Author or Editor: Minghua Zhang x
  • Monthly Weather Review x
  • Refine by Access: Content accessible to me x
Clear All Modify Search
Jia Wang
and
Minghua Zhang

Abstract

A constrained data assimilation (CDA) system based on the ensemble variational (EnVar) method and physical constraints of mass and water conservations is evaluated through three convective cases during the Midlatitude Continental Convective Clouds Experiment (MC3E) of the Atmospheric Radiation Measurement (ARM) program. Compared to the original data assimilation (ODA), the CDA is shown to perform better in the forecasted state variables and simulated precipitation. The CDA is also shown to greatly mitigate the loss of forecast skills in observation denial experiments when radar radial winds are withheld in the assimilation. Modifications to the algorithm and sensitivities of the CDA to the calculation of the time tendencies in the constraints are described.

Free access
Jia Wang
and
Minghua Zhang

Abstract

Data assimilation (DA) at mesoscales is important for severe weather forecasts, yet the techniques of data assimilation at this scale remain a challenge. This study introduces dynamical constraints in the Gridpoint Statistical Interpolation (GSI) three-dimensional ensemble variational (3D-EnVar) data assimilation algorithm to enable the use of high-resolution surface observations of precipitation to improve atmospheric analysis at mesoscales. The constraints use the conservations of mass and moisture. Mass constraint suppresses the unphysical high-frequency oscillation, while moisture conservation constrains the atmospheric states to conform with the observed high-resolution precipitation. We show that the constrained data assimilation (CDA) algorithm significantly reduced the spurious residuals of the mass and moisture budgets compared to the original data assimilation (ODA). A case study is presented for a squall line over the Southern Great Plains on 20 May 2011 during Midlatitude Continental Convective Clouds Experiment (MC3E) of the Atmospheric Radiation Measurement (ARM) program by using ODA or CDA analysis as initial condition of forecasts. The state variables, and the location and intensity of the squall line are better simulated in the CDA experiment. Results show how surface observation of precipitation can be used to improve atmospheric analysis through data assimilation by using the dynamical constraints of mass and moisture conservations.

Full access
He Zhang
,
Minghua Zhang
, and
Qing-cun Zeng

Abstract

The dynamical core of the Institute of Atmospheric Physics of the Chinese Academy of Sciences Atmospheric General Circulation Model (IAP AGCM) and the Eulerian spectral transform dynamical core of the Community Atmosphere Model, version 3.1 (CAM3.1), developed at the National Center for Atmospheric Research (NCAR) are used to study the sensitivity of simulated climate. The authors report that when the dynamical cores are used with the same CAM3.1 physical parameterizations of comparable resolutions, the model with the IAP dynamical core simulated a colder troposphere than that from the CAM3.1 core, reducing the CAM3.1 warm bias in the tropical and midlatitude troposphere. However, when the two dynamical cores are used in the idealized Held–Suarez tests without moisture physics, the IAP AGCM core simulated a warmer troposphere than that in CAM3.1. The causes of the differences in the full models and in the dry models are then investigated.

The authors show that the IAP dynamical core simulated weaker eddies in both the full physics and the dry models than those in the CAM due to different numerical approximations. In the dry IAP model, the weaker eddies cause smaller heat loss from poleward dynamical transport and thus warmer troposphere in the tropics and midlatitudes. When moist physics is included, however, weaker eddies also lead to weaker transport of water vapor and reduction of high clouds in the IAP model, which then causes a colder troposphere due to reduced greenhouse warming of these clouds. These results show how interactive physical processes can change the effect of a dynamical core on climate simulations between two models.

Full access
Courtney Schumacher
,
Paul E. Ciesielski
, and
Minghua H. Zhang

Abstract

Diabatic heating (or Q 1) profiles associated with specific cloud types are produced by matching synoptic cloud observations with a sounding budget analysis during the Tropical Rainfall Measuring Mission (TRMM) Kwajalein Experiment (KWAJEX), which took place in the Marshall Islands from late July through mid-September 1999. Fair-weather cumulus clouds produce up to 1 K day−1 of heating below 850 hPa and are associated with cooling throughout much of the rest of the troposphere. Cumulus congestus clouds produce heating on the order of 1 K day−1 up to 575 hPa and cooling in the mid- to upper troposphere. Cumulonimbus clouds produce heating through the depth of the troposphere, with a maximum of 3.7 K day−1 near 550 hPa. Cloud types indicating widespread rain (stratus or cumulus fractus of bad weather at low levels and nimbostratus at midlevels) have the largest and most elevated heating, with values >10 K day−1 above 600 hPa. Other mid- and high-level cloud types are shown to be consistent with area-averaged rain rates and Q 1 profiles. Profiles of the divergence and apparent moisture sink (or Q 2) for convective clouds are also analyzed and are shown to be consistent with the physics of the heating profiles just described.

Full access
Ping Liu
,
Qin Zhang
,
Chidong Zhang
,
Yuejian Zhu
,
Marat Khairoutdinov
,
Hye-Mi Kim
,
Courtney Schumacher
, and
Minghua Zhang

Abstract

This study investigates why OLR plays a small role in the Real-time Multivariate (Madden–Julian oscillation) MJO (RMM) index and how to improve it. The RMM index consists of the first two leading principal components (PCs) of a covariance matrix, which is constructed by combined daily anomalies of OLR and zonal winds at 850 (U850) and 200 hPa (U200) in the tropics after being normalized with their globally averaged standard deviations of 15.3 W m−2, 1.8 m s−1, and 4.9 m s−1, respectively. This covariance matrix is reasoned mathematically close to a correlation matrix. Both matrices substantially suppress the overall contribution of OLR and make the index more dynamical and nearly transparent to the convective initiation of the MJO. A covariance matrix that does not use normalized anomalies leads to the other extreme where OLR plays a dominant role while U850 and U200 are minor. Numerous tests indicate that a simple scaling of the anomalies (i.e., 2 W m−2, 1 m s−1, and 1 m s−1) can better balance the roles of OLR and winds. The revised PCs substantially enhance OLR over the eastern Indian and western Pacific Oceans and change it less notably in other locations, while they reduce U850 and U200 only slightly. Comparisons with the original RMM in spatial structure, power spectra, and standard deviation demonstrate improvements of the revised RMM index.

Full access