Search Results

You are looking at 41 - 46 of 46 items for :

  • Author or Editor: T. N. Krishnamurti x
  • Monthly Weather Review x
  • Refine by Access: All Content x
Clear All Modify Search
Mukut B. Mathur
,
H. S. Bedi
,
T. N. Krishnamurti
,
Masao Kanamitsu
, and
Jack S. Woollen

Abstract

Sparsity of conventional data over tropical oceans makes it difficult to analyze well the moisture and divergence fields, and therefore the diabatic forcing of the tropical atmosphere is not well predicted in numerical models. A nudging procedure to improve the precipitation forecast in the National Meteorological Center (NMC) Medium Range Forecast Model (MRF) is developed. The convective parameterization scheme is modified to adjust the predicted rainfall amounts toward the observations in this method. In the absence of conventional data, the rainfall estimates from the satellite measures of the outward-going longwave radiation are utilized as the observed precipitation.

Several forecasts from the MRF are presented to show the improvements in intensity and location of the intertropical convergence zone and tropical disturbances with the application of the nudging procedure. Additionally, spurious cyclone and excessive rainfall that were predicted without this procedure either failed to form or their intensifies were considerably reduced.

Results from incorporation of the modified convective scheme in the global data-assimilation system within the NMC forecast model are also discussed. The analysis, the subsequent 72-h forecast circulation, and the rainfall amounts are improved with the use of this scheme.

Full access
Saad Mohalfi
,
H. S. Bedi
,
T. N. Krishnamurti
, and
Steven D. Cocke

Abstract

A two-stream scattering scheme based on the delta-Eddington approximation is incorporated into the Florida State University Limited Area Model for computing the shortwave radiative fluxes due to dust aerosols over the Saudi Arabian region and to study their impact on synoptic-scale systems and the diurnal cycle over the region. The radiative properties of dust corresponding to different categories of dustiness are determined from the results of field experiments. Satellite imagery and visibility are used to determine the intensity and extent of the dust layer.

Two parallel simulations, one including the radiative effects of dust aerosols and the other without them, were made over a 6-day period starting with 1200 UTC 25 June 1979 using First GARP (Global Atmospheric Research Program) Global Experiment IIIb data analyses from ECMWF. A comparison of the two experiments shows that the dust aerosol radiative heating strengthens the heat low over Saudi Arabia. Furthermore, the radiative heating of the heavy dust concentrated at low levels during the dust outbreak episode protects the heat low from its possible destruction due to strong cold winds from the northwest.

A significant improvement in the diurnal cycle of temperature at middle levels occurs with the introduction of dust aerosols. The extension of the dust layer over the Arabian Sea also warms the middle levels in the vicinity of the dust layer and cools the layer below it, thus intensifying the inversion above the monsoon flow. The presence of dust aerosols over the Arabian Sea is also found to affect the intensity of the low-level Somali jet and the diurnal cycle of the sea breeze. These model results are found to be consistent with observations.

Full access
T. N. Krishnamurti
,
K. Rajendran
,
T. S. V. Vijaya Kumar
,
Stephen Lord
,
Zoltan Toth
,
Xiaolei Zou
,
Steven Cocke
,
Jon E. Ahlquist
, and
I. Michael Navon

Abstract

This paper addresses the anomaly correlation of the 500-hPa geopotential heights from a suite of global multimodels and from a model-weighted ensemble mean called the superensemble. This procedure follows a number of current studies on weather and seasonal climate forecasting that are being pursued. This study includes a slightly different procedure from that used in other current experimental forecasts for other variables. Here a superensemble for the ∇2 of the geopotential based on the daily forecasts of the geopotential fields at the 500-hPa level is constructed. The geopotential of the superensemble is recovered from the solution of the Poisson equation. This procedure appears to improve the skill for those scales where the variance of the geopotential is large and contributes to a marked improvement in the skill of the anomaly correlation. Especially large improvements over the Southern Hemisphere are noted. Consistent day-6 forecast skill above 0.80 is achieved on a day to day basis. The superensemble skills are higher than those of the best model and the ensemble mean. For days 1–6 the percent improvement in anomaly correlations of the superensemble over the best model are 0.3, 0.8, 2.25, 4.75, 8.6, and 14.6, respectively, for the Northern Hemisphere. The corresponding numbers for the Southern Hemisphere are 1.12, 1.66, 2.69, 4.48, 7.11, and 12.17. Major improvement of anomaly correlation skills is realized by the superensemble at days 5 and 6 of forecasts. The collective regional strengths of the member models, which is reflected in the proposed superensemble, provide a useful consensus product that may be useful for future operational guidance.

Full access
T. N. Krishnamurti
,
S. Pattnaik
,
L. Stefanova
,
T. S. V. Vijaya Kumar
,
B. P. Mackey
,
A. J. O’Shay
, and
Richard J. Pasch

Abstract

The intensity issue of hurricanes is addressed in this paper using the angular momentum budget of a hurricane in storm-relative cylindrical coordinates and a scale-interaction approach. In the angular momentum budget in storm-relative coordinates, a large outer angular momentum of the hurricane is depleted continually along inflowing trajectories. This depletion occurs via surface and planetary boundary layer friction, model diffusion, and “cloud torques”; the latter is a principal contributor to the diminution of outer angular momentum. The eventual angular momentum of the parcel near the storm center determines the storm’s final intensity. The scale-interaction approach is the familiar energetics in the wavenumber domain where the eddy and zonal kinetic energy on the hurricane scale offer some insights on its intensity. Here, however, these are cast in storm-centered local cylindrical coordinates as a point of reference. The wavenumbers include azimuthally averaged wavenumber 0, principal hurricane-scale asymmetries (wavenumbers 1 and 2, determined from datasets) and other scales. The main questions asked here relate to the role of the individual cloud scales in supplying energy to the scales of the hurricane, thus contributing to its intensity. A principal finding is that cloud scales carry most of their variance, via organized convection, directly on the scales of the hurricane. The generation of available potential energy and the transformation of eddy kinetic energy from the cloud scale are in fact directly passed on to the hurricane scale by the vertical overturning processes on the hurricane scale. Less of the kinetic energy is generated on the scales of individual clouds that are of the order of a few kilometers. The other major components of the energetics are the kinetic-to-kinetic energy exchange and available potential-to-available potential energy exchange among different scales. These occur via triad interaction and were noted to be essentially downscale transfer, that is, a cascading process. It is the balance among these processes that seems to dictate the final intensity.

Full access
C. Eric Williford
,
T. N. Krishnamurti
,
Ricardo Correa Torres
,
Steven Cocke
,
Zaphiris Christidis
, and
T. S. Vijaya Kumar

Abstract

In this paper, Atlantic hurricane forecasts for the year 1999 are addressed. The methodology for these forecasts is called the multimodel superensemble. This statistical method makes use of the real-time forecasts provided by a number of operational and research models to construct the superensemble forecasts. This method divides the forecast time line into two phases: a training phase and a forecast combining phase. The training phase includes an inventory of past applicable hurricane forecasts, each by the multimodels. The model biases of position and intensity errors of past forecasts are summarized via a simple linear multiple regression of these forecasts against the best-observed estimates of position and intensity. These statistics are next passed on to future forecasts of the multimodels in order to forecast the hurricanes of 1999. This method was first tested for the hurricanes of 1998 with considerable success, with some of those results summarized here. Those statistics were refined for the 1999 Atlantic hurricane season. Overall, the main result of the seasonal summary is that the position and intensity errors for the multimodel superensemble are generally less than those of all of the participating models during 1–5-day real-time forecasts. Some of the major storms of the 1999 season, such as Dennis, Floyd, Irene, and Lenny, were extremely well handled by this superensemble approach. The message of this study is that the proposed approach may be a viable way to construct improved real-time forecasts of hurricane positions and intensity.

Full access
T. N. Krishnamurti
,
Sajani Surendran
,
D. W. Shin
,
Ricardo J. Correa-Torres
,
T. S. V. Vijaya Kumar
,
Eric Williford
,
Chris Kummerow
,
Robert F. Adler
,
Joanne Simpson
,
Ramesh Kakar
,
William S. Olson
, and
F. Joseph Turk

Abstract

This paper addresses real-time precipitation forecasts from a multianalysis–multimodel superensemble. The methodology for the construction of the superensemble forecasts follows previous recent publications on this topic. This study includes forecasts from multimodels of a number of global operational centers. A multianalysis component based on the Florida State University (FSU) global spectral model that utilizes TRMM and SSM/I datasets and a number of rain-rate algorithms is also included. The difference in the analysis arises from the use of these rain rates within physical initialization that produces distinct differences among these components in the divergence, heating, moisture, and rain-rate descriptions. A total of 11 models, of which 5 represent global operational models and 6 represent multianalysis forecasts from the FSU model initialized by different rain-rate algorithms, are included in the multianalysis–multimodel system studied here. In this paper, “multimodel” refers to different models whose forecasts are being assimilated for the construction of the superensemble. “Multianalysis” refers to different initial analysis contributing to forecasts from the same model. The term superensemble is being used here to denote the bias-corrected forecasts based on the products derived from the multimodel and the multianalysis. The training period is covered by nearly 120 forecast experiments prior to 1 January 2000 for each of the multimodels. These are all 3-day forecasts. The statistical bias of the models is determined from multiple linear regression of these forecasts against a “best” rainfall analysis field that is based on TRMM and SSM/I datasets and using the rain-rate algorithms recently developed at NASA Goddard Space Flight Center. This paper discusses the results of real-time rainfall forecasts based on this system. The main results of this study are that the multianalysis–multimodel superensemble has a much higher skill than the participating member models. The skill of this system is higher than those of the ensemble mean that assigns a weight of 1.0 to all including the poorer models and the ensemble mean of bias-removed individual models. The selective weights for the entire multianalysis–multimodel superensemble forecast system make it superior to individual models and the above mean representations. The skill of precipitation forecasts is addressed in several ways. The skill of the superensemble-based rain rates is shown to be higher than the following: (a) individual model's skills with and without physical initialization, (b) skill of the ensemble mean, and (c) skill of the ensemble mean of individually bias-removed models.

The equitable-threat scores at many thresholds of rain are also examined for the various models and noted that for days 1–3 of forecasts, the superensemble-based forecasts do have the highest skills. The training phase is a major component of the superensemble. Issues on optimizing the number of training days is addressed by examining training with days of high forecast skill versus training with low forecast skill, and training with the best available rain-rate datasets versus those from poor representations of rain. Finally the usefulness of superensemble forecasts of rain for providing possible guidance for flood events such as the one over Mozambique during February 2000 is shown.

Full access