Search

The performance of revised climate forecast system version 2 (CFSv2) are evaluated on the simulation of the underlying cloud and convective processes associated with the strong and weak boreal summer intraseasonal oscillations (BSISOs) events. The revised version of the CFSv2 consists of a six-class Weather Research Forecasting single moment (WSM6) cloud microphysics scheme and the default version has Zhao and Carr (ZC) cloud microphysics scheme. Both the version uses revised simplified Arakawa-Schubert (RSAS) convection scheme. The study reveals that the revised version of CFSv2 (RSAS-WSM) is able to better simulate the northward propagation of BSISOs and associated dynamical and thermodynamical mechanism put forward by earlier observation-based studies. It is found that the large-scale organized northwest-southeast tilted structure of rain band is better captured in RSAS-WSM simulation as compared to the default version of CFSv2 (RSAS-ZC) during strong BSISO events. Further, the reasonable large-scale or stratiform rainfall associated with the northward propagating strong BSISO events is seen in RSAS-WSM while it is completely missing in RSAS-ZC simulation. The pressure-latitude profiles of cloud liquid water (CLW) and cloud ice (CLI) show more realistic steady northward propagation in RSAS-WSM simulation. Consistent with the CLW and CLI distribution and their influence on the large-scale heating structure, the large-scale condensation heating shows quasi-periodic northward propagation in RSAS-WSM whereas such type of distribution is not captured in RSAS-ZC simulation. The realistic representation of cloud processes in WSM leads to simulate reasonable dynamical and thermodynamical processes associated with the strong BSISO events which follows the observation-based hypothesis proposed by earlier studies. [ABSTRACT FROM AUTHOR]

This study focuses on the short-range prediction of Monsoon Intraseasonal Oscillations (MISOs) using the National Centers for Environmental Prediction(NCEP) Ensemble Prediction System (EPS) data from The Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE) archive. The Indian Summer Monsoon Rainfall (ISMR), which plays an important role in the socio-economic growth of the country, is highly variable and is mostly governed by the MISOs. In addition to this, deterministic forecasts of ISMR are not very reliable. Hence, a probabilistic approach at daily scale is required. Keeping this in mind, the present analysis is done by using daily forecast data for up to 7-day lead time and compared with observations. The analysis shows that the ensemble forecast well captures the variability as compared to observations even up to 7 days. The spatial characteristics and the northward propagation of MISO are observed thoroughly in the EPS. The evolution of dynamical and thermodynamical parameters such as specific humidity, moist static energy, moisture divergence, and vorticity is also captured well but show deviation from the observation from 96 h lead time onwards. The tropospheric temperature forecast captures the observed gradient but with certain bias in magnitude whereas the wind shear is simulated quite well both in pattern and magnitude. These analyses bring out the biases in TIGGE EPS forecast and also point out the possible moist processes which needs to be improved. [ABSTRACT FROM AUTHOR]

The present study examines the ability of high resolution (T382) National Centers for Environmental Prediction coupled atmosphere-ocean climate forecast system version 2 (CFS T382) in simulating the salient spatio-temporal characteristics of the boreal summertime mean climate and the intraseasonal variability. The shortcomings of the model are identified based on the observation and compared with earlier reported biases of the coarser resolution of CFS (CFS T126). It is found that the CFS T382 reasonably mimics the observed features of basic state climate during boreal summer. But some prominent biases are noted in simulating the precipitation, tropospheric temperature (TT) and sea surface temperature (SST) over the global tropics. Although CFS T382 primarily reproduces the observed distribution of the intraseasonal variability over the Indian summer monsoon region, some difficulty remains in simulating the boreal summer intraseasonal oscillation (BSISO) characteristics. The simulated eastward propagation of BSISO decays rapidly across the Maritime Continent, while the northward propagation appears to be slightly slower than observation. However, the northward propagating BSISO convection propagates smoothly from the equatorial region to the northern latitudes with observed magnitude. Moreover, the observed northwest-southeast tilted rain band is not well reproduced in CFS T382. The warm mean SST bias and inadequate simulation of high frequency modes appear to be responsible for the weak simulation of eastward propagating BSISO. Unlike CFS T126, the simulated mean SST and TT exhibit warm biases, although the mean precipitation and simulated BSISO characteristics are largely similar in both the resolutions of CFS. Further analysis of the convectively coupled equatorial waves (CCEWs) indicates that model overestimates the gravest equatorial Rossby waves and underestimates the Kelvin and mixed Rossby-gravity waves. Based on analysis of CCEWs, the study further explains the possible reasons behind the realistic simulation of northward propagating BSISO in CFS T382, even though the model shows substantial biases in simulating mean state and other BSISO modes. [ABSTRACT FROM AUTHOR]

Keeping the systematic bias of the climate forecast system model version 2 (CFSv2) in mind, an attempt is made to improve the Indian summer monsoon (ISM) rainfall variability in the model from diurnal through daily to seasonal scale. Experiments with default simplified Arakawa-Schubert (SAS) and a revised SAS schemes are carried out to make 15 years climate run (free run) to evaluate the model fidelity with revised SAS as compared to default SAS. It is clearly seen that the revised SAS is able to reduce some of the biases of CFSv2 with default SAS. Improvement is seen in the annual seasonal cycle, onset and withdrawal but most importantly the rainfall probability distribution function (PDF) has improved significantly. To understand the reason behind the PDF improvement, the diurnal rainfall simulation is analysed and it is found that the PDF of diurnal rainfall has significantly improved with respect to even a high resolution CFSv2 T382 version. In the diurnal run with revised SAS, the PDF of rainfall over central India has remarkably improved. The improvement of diurnal cycle of total rainfall has actually been contributed by the improvement of diurnal cycle of convection and associated convective rainfall. This is reflected in outgoing longwave radiation and high cloud diurnal cycle. This improvement of convective cycle has resolved a long standing problem of dry bias by CFSv2 over Indian land mass and wet bias over equatorial Indian Ocean. Besides the improvement, there are some areas where there are still scopes for further development. The cold tropospheric temperature bias, low cloud fractions need further improvement. To check the role of shallow convection, another free run is made with revised SAS along with shallow convection (SC). The major difference between the new and old SC schemes lies in the heating and cooling behavior in lower-atmospheric layers above the planetary boundary layer. However, the inclusion of revised SC scheme could not show much improvement as compared to revised SAS with deep convection. Thus, it seems that revised SAS with deep convection can be a potentially better parameterization scheme for CFSv2 in simulating ISM rainfall variability. [ABSTRACT FROM AUTHOR]

A global forecast system model at a horizontal resolution of T1534 ( ∼ 12.5 km ) has been evaluated for the monsoon seasons of 2016 and 2017 over the Indian region. It is for the first time that such a high-resolution global model is being run operationally for monsoon weather forecast. A detailed validation of the model therefore is essential. The validation of mean monsoon rainfall for the season and individual months indicates a tendency for wet bias over the land region in all the forecast lead time. The probability distribution of forecast rainfall shows an overestimation (underestimation) of rainfall for the lighter (heavy) categories. However, the probability distribution functions of moderate rainfall categories are found to be reasonable. The model shows fidelity in capturing the extremely heavy rainfall categories with shorter lead times. The model reasonably predicts the large-scale parameters associated with the Indian summer monsoon, particularly, the vertical profile of the moisture. The diurnal rainfall variability forecasts in all lead times show certain biases over different land and oceanic regions and, particularly, over the north–west Indian region. Although the model has a reasonable fidelity in capturing the spatio-temporal variability of the monsoon rain, further development is needed to enhance the skill of forecast of a higher rain rate with a longer lead time. [ABSTRACT FROM AUTHOR]

The article discusses a workshop on improvement in climate and weather models that was held from February 13-16, 2018, at Indian Institute of Tropical Meteorology, Pune.

A Winter Fog Experiment (WiFEX) was conducted to study the genesis of fog formation between winters 2016–17 and 2017–18 at Indira Gandhi International Airport (IGIA), Delhi, India. To support the WiFEX field campaign, the Weather Research and Forecasting (WRF) Model was used to produce real-time forecasts at 2-km horizontal grid spacing. This paper summarizes the performance of the model forecasts for 43 very dense fog episodes (visibility < 200 m) and preliminary evaluation of the model against the observations. Similarly, near-surface liquid water content (LWC) from models and continuous visibility observations are used as a metric for model evaluation. Results show that the skill score is relatively promising for the hit rate with a value of 0.78, whereas the false alarm rate (0.19) and missing rate (0.32) are quite low. This indicates that the model has reasonable predictive accuracy, and the performance of the real-time forecast is better for both dense fog events and no-fog events. For success cases, the model accurately captured the near-surface meteorological conditions, particularly the low-level moisture, wind fields, and temperature inversion. In contrast, for failed cases, the WRF Model shows large error in near-surface relative humidity and temperature compared to the observations, although it captures temperature inversions reasonably well. Our results also suggest that the model is able to capture the variability in fog onset for consecutive fog events. Errors in near-surface variables during failed cases are found to be affected by the errors in the initial conditions taken from the Indian Institute of Tropical Meteorology Global Forecasting System (IITM-GFS) spectral model forecast. Further evaluation of the operational forecasts for dense fog cases indicates that the error in predicting fog onset stage is relatively large (mean error of 4 h) compared to the dissipation stage. [ABSTRACT FROM AUTHOR]

A stochastic multicloud model (SMCM) convective parameterization, which mimics the interactions at subgrid scales of multiple cloud types, is incorporated into the National Centers for Environmental Prediction (NCEP) Climate Forecast System, version 2 (CFSv2), model (CFSsmcm) in lieu of the preexisting simplified Arakawa-Schubert (SAS) cumulus scheme. A detailed analysis of the tropical intraseasonal variability (TISV) and convectively coupled equatorial waves (CCEW) in comparison with the original (control) model and with observations is presented here. The last 10 years of a 15-yr-long climate simulation are analyzed. Significant improvements are seen in the simulation of the Madden-Julian oscillation (MJO) and most of the CCEWs as well as the Indian summer monsoon (ISM) intraseasonal oscillation (MISO). These improvements appear in the form of improved morphology and physical features of these waves. This can be regarded as a validation of the central idea behind the SMCM according to which organized tropical convection is based on three cloud types, namely, the congestus, deep, and stratiform cloud decks, that interact with each other and form a building block for multiscale convective systems. An adequate accounting of the dynamical interactions of this cloud hierarchy thus constitutes an important requirement for cumulus parameterizations to succeed in representing atmospheric tropical variability. SAS fails to fulfill this requirement, which is evident in the unrealistic physical structures of the major intraseasonal modes simulated by CFSv2 as documented here. [ABSTRACT FROM AUTHOR]