Does bias correction in the forecasted <scp>SST</scp> improve the extended range prediction skill of active‐break spells of Indian summer monsoon rainfall?

Abhilash, S.; Sahai, A. K.; Borah, N.; Chattopadhyay, Rajib; Joseph, Susmitha; Sharmila, S.; De, S.; Goswami, B. N.

doi:10.1002/asl2.477

Cited by 40 publications

(28 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The application of flux and SST bias-correction techniques during the forecast system integrations is shown to improve the extended range rain forecasts by Abhilash et al (2013) and Borah et al (2015). However, such an analysis is beyond the scope of the current study since the main aim here is to highlight process biases producing a dry land bias at seasonal timescales.…”

Section: Discussionmentioning

confidence: 95%

Ocean–atmosphere processes driving Indian summer monsoon biases in CFSv2 hindcasts

et al. 2015

View full text Add to dashboard Cite

Section: Discussionmentioning

confidence: 95%

Ocean–atmosphere processes driving Indian summer monsoon biases in CFSv2 hindcasts

et al. 2015

View full text Add to dashboard Cite

“…We denote this two-tier forecast as GFSbc, with 'bc' indicating bias-corrected boundary conditions. For more details readers can refer to Abhilash et al 13 . The other two versions of CFS run independently are the CFS T126 (~110 km) and the CFS T382 (~38 km) runs.…”

Section: Model and Forecast Strategymentioning

confidence: 99%

“…orography) and the large-scale processes are well represented in them. Further model and experimental details and skills of GFSbc, CFST126 and CFST382 are available in the literature [13][14][15] . Based on performance experience and aiming to maximize the operational skill depending on computer resources, we choose to pool these variants: 11 members of CFST126, 11 members of CFST382 and 21 members of GFSbc.…”

Section: Model and Forecast Strategymentioning

confidence: 99%

“…The phrase 'multi-model' here refers to (i) the same model run with two different spatial resolutions (T126 and T382), and (ii) the GFS model (essentially the atmospheric version of the CFSv2) run with sea surface temperature (SST) generated from the CFSv2 forecast runs with some bias correction 13 . The pros and cons of the individual components of the CFSv2 as referred above are described in several recent studies [12][13][14] . The phrase 'muti-ensemble' here refers to the ensemble of runs generated from the perturbed initial conditions (ICs) for each participating model.…”

mentioning

confidence: 99%

“…The phrase 'muti-ensemble' here refers to the ensemble of runs generated from the perturbed initial conditions (ICs) for each participating model. The method of generating the perturbed initial conditions has been discussed in earlier studies 7,13,15 and also briefly later in the text. This article deals with the extended range forecast (i.e.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Real-Time Performance of a Multi-Model Ensemble-Based Extended Range Forecast System in Predicting the 2014 Monsoon Season Based on NCEP-CFSv2

Sahai¹,

Chattopadhyay²,

Joseph³

et al. 2015

Current Science

Self Cite

View full text Add to dashboard Cite

The real-time validation of any strategy to forecast the Indian summer monsoon rainfall requires comprehensive assessment of performance of the model on subseasonal scale. The CFSv2-based grand multi-model ensemble (CGMME) approach based on the NCEP-CFS version 2 models, as developed and reported earlier, has been employed to forecast the 2014 monsoon season on the extended range scale with 3-4 pentad lead time (where a pentad corresponds to five-day average). The present study reports the broad performance of the CGMME employed on experimental basis to forecast the salient features of the real-time evolution of the 2014 monsoon season during June to September. The CGMME is successful in predicting both these features well in advance. The assessment of the model performance at pentad scale lead time shows that the weak monsoon conditions that are evident in precipitation and lower level wind anomalies are well captured as a whole up to four pentad advance lead time. The subseasonal propagation during onset and withdrawal is also evident in the forecast. Finally, the region-wise performance shows that the spatial extent of the skillful forecast encompasses central India as well as the monsoon zone for the 2014 monsoon season. Considering the natural variation in the forecast skill of extended range forecast itself as reported in earlier studies, the 2014 monsoon forecast seems to be skillful for operational purposes. For other regions (e.g. North East India), the forecast could be skillful at times, but it still requires further research on how to improve the same.

show abstract

Bay of Bengal salinity stratification and Indian summer monsoon intraseasonal oscillation: 2. Impact on SST and convection

Han

Wang

et al. 2017

JGR Oceans

View full text Add to dashboard Cite

The Indian summer monsoon intraseasonal oscillations (MISOs) induce pronounced intraseasonal sea surface temperature (SST) variability in the Bay of Bengal (BoB), which has important feedbacks to atmospheric convection. An ocean general circulation model (OGCM) is employed to investigate the upper‐ocean processes affecting intraseasonal SST variability and its feedback to the MISO convection. In the BoB, the MISO induces intraseasonal SST variability predominantly through surface heat flux forcing with comparable contributions from shortwave radiation and turbulent heat flux, and to a much smaller extent through wind‐driven ocean mixed layer entrainment. The ocean salinity stratification, represented by mixed layer depth (MLD) and barrier layer thickness (BLT), has a strong control on SST but weak impact on convection of the MISO. The MLD is critical for the amplitude of SST response to various forcing processes, while the BLT mainly affects entrainment by determining the temperature difference between the mixed layer and the water below. From May to mid‐June, the shallow MLD and thin barrier layer greatly enhance intraseasonal SST anomalies, which can amplify convection fluctuations of the MISO through air‐sea interaction and leads to intense but short‐duration postconvection break spells. When either the MLD or the BLT is large, intraseasonal SSTs tend to be weak. Further investigation reveals that freshwater flux of the monsoon gives rise to the shallow MLD and thick barrier layer, and its overall effect on intraseasonal SSTs is a 20% enhancement. These results provide implications for improving the simulation and forecast of the MISO in climate models.

show abstract

Does bias correction in the forecasted SST improve the extended range prediction skill of active‐break spells of Indian summer monsoon rainfall?

Cited by 40 publications

References 17 publications

Ocean–atmosphere processes driving Indian summer monsoon biases in CFSv2 hindcasts

Ocean–atmosphere processes driving Indian summer monsoon biases in CFSv2 hindcasts

Real-Time Performance of a Multi-Model Ensemble-Based Extended Range Forecast System in Predicting the 2014 Monsoon Season Based on NCEP-CFSv2

Bay of Bengal salinity stratification and Indian summer monsoon intraseasonal oscillation: 2. Impact on SST and convection

Contact Info

Product

Resources

About