Forecast Guidance Research Articles

Applying Satellite Observations of Tropical Cyclone Internal Structures to Rapid Intensification Forecast With Machine Learning

AbstractTropical cyclone (TC) intensity change is controlled by both environmental conditions and internal storm processes. We show that TC 24‐hr subsequent intensity change (DV24) is linearly correlated with the departures in satellite observations of inner‐core precipitation, ice water content, and outflow temperature from respective threshold values corresponding to neutral TCs of nearly constant intensity. The threshold values vary linearly with TC intensity. Using machine learning with the inner‐core precipitation and the predictors currently employed at the National Hurricane Center (NHC) for probabilistic rapid intensification (RI) forecast guidance, our model outperforms the NHC operational RI consensus in terms of the Peirce Skill Score for RI in the Atlantic basin during 2009–2014 by 37%, 12%, and 138% for DV24 ≥ 25, 30, and 35 kt, respectively. Our probability of detection is 40%, 60%, and 200% higher than the operational RI consensus, while the false alarm ratio is only 4%, 7%, and 6% higher.

A high-amplitude atmospheric inertia–gravity wave-induced meteotsunami in Lake Michigan

On Friday, April 13, 2018, a high-amplitude atmospheric inertia–gravity wave packet with surface pressure perturbations exceeding 10 mbar crossed the lake at a propagation speed that neared the long-wave gravity speed of the lake, likely producing Proudman resonance. A set of meteotsunami waves struck the shores near Ludington, Michigan, a coastal community along the sandy dunes of Lake Michigan. During the event, harbor walls were overtopped, damage occurred to shoreline homes and boat docks, and water intake pumps were impacted due to the large change in water level. To fully understand the generation of this event and the impacts to the coastal community, we have carried out atmospheric and hydrodynamic model simulations of the inertia–gravity and meteotsunami waves. Atmospheric simulation of the inertia–gravity waves was performed using a high-resolution model for the Great Lakes region that mimics the National Oceanic and Atmospheric Administration High-Resolution Rapid Refresh operational model. Surface meteorological conditions were supplied to the Lake Michigan-Huron Operational Forecast System, an operational model used for hydrodynamic forecast guidance. This is the first documented case of a meteotsunami generated by an atmospheric inertia–gravity wave in the Great Lakes, and it provides an evaluation of existing and proposed operational infrastructure as it pertains to meteotsunami forecasting in the USA.

Evaluation of IMERG-E Precipitation Estimates for Fire Weather Applications in Alaska

AbstractLarge wildfire outbreaks in Alaska are common from June to August. The Canadian Forest Fire Danger Rating System (CFFDRS) is used operationally by Alaskan fire managers to produce statewide fire weather outlooks and forecast guidance near active wildfires. The CFFDRS estimates of fire potential and behavior rely heavily on meteorological observations (precipitation, temperature, wind speed, and relative humidity) from the relatively small number of in situ stations across Alaska with precipitation being the most critical parameter. To improve the spatial coverage of precipitation estimates across Alaska for fire weather applications, a multisatellite precipitation algorithm was evaluated during six fire seasons (1 June–31 August 2014–19). Near-real-time daily precipitation estimates from the Integrated Multisatellite Retrievals for the Global Precipitation Mission (IMERG) algorithm were verified using 322 in situ stations across four Alaskan regions. For each region, empirical cumulative distributions of daily precipitation were obtained from station observations during each summer, and compared to corresponding distributions of interpolated values from IMERG grid points (0.1° × 0.1° grid). The cumulative distributions obtained from IMERG exhibited wet biases relative to the observed distributions for all regions, precipitation amount ranges, and summers. A bias correction approach using regional quantile mapping was developed to mitigate for the IMERG wet bias. The bias-adjusted IMERG daily precipitation estimates were then evaluated and found to produce improved gridded IMERG precipitation estimates. This approach may help to improve situational awareness of wildfire potential across Alaska and be appropriate for other high-latitude regions where there are sufficient in situ precipitation observations to help correct the IMERG precipitation estimates.

A Comparison of Tropical Cyclone Genesis Forecast Verification from Three Global Forecast System (GFS) Operational Configurations

AbstractOperational forecasting of tropical cyclone (TC) genesis has improved in recent years but still can be a challenge. Output from global numerical models continues to serve as a primary source of forecast guidance. Bulk verification statistics (e.g., critical success index) of TC genesis forecasts indicate that, overall, global models are increasingly able to predict TC genesis. However, as global model configurations are updated, TC genesis verification statistics will change. This study compares operational and retrospective forecasts from three configurations of NCEP’s Global Forecast System (GFS) to quantify the impact of model upgrades on TC genesis forecasts. First, bulk verification statistics from a homogeneous sample of model initialization cycles during the period 2013–14 are compared. Then, composites of select output fields are analyzed in an attempt to identify any key differences between hit and false alarm events. Bulk statistics indicate that TC genesis forecast performance decreased with the implementation of the 2015 version of the GFS, but then modestly recovered with the 2016 version of the model. In addition, the composite analysis suggests that false alarm forecasts in the 2015 version of the GFS may have been the result of inaccurately forecasting the location and/or strength of upper-level troughs poleward of the TC. There is also evidence of convective feedbacks occurring, such as ridging above the low-level circulation and upper-level convective outflow that were too strong, in this same set of false alarm forecasts. Overall, analyzing retrospective forecasts can assist forecasters in determining the strengths and weaknesses associated with a new configuration of a global model with respect to TC genesis.

Generating Probabilistic Next-Day Severe Weather Forecasts from Convection-Allowing Ensembles Using Random Forests

AbstractExtracting explicit severe weather forecast guidance from convection-allowing ensembles (CAEs) is challenging since CAEs cannot directly simulate individual severe weather hazards. Currently, CAE-based severe weather probabilities must be inferred from one or more storm-related variables, which may require extensive calibration and/or contain limited information. Machine learning (ML) offers a way to obtain severe weather forecast probabilities from CAEs by relating CAE forecast variables to observed severe weather reports. This paper develops and verifies a random forest (RF)-based ML method for creating day 1 (1200–1200 UTC) severe weather hazard probabilities and categorical outlooks based on 0000 UTC Storm-Scale Ensemble of Opportunity (SSEO) forecast data and observed Storm Prediction Center (SPC) storm reports. RF forecast probabilities are compared against severe weather forecasts from calibrated SSEO 2–5-km updraft helicity (UH) forecasts and SPC convective outlooks issued at 0600 UTC. Continuous RF probabilities routinely have the highest Brier skill scores (BSSs), regardless of whether the forecasts are evaluated over the full domain or regional/seasonal subsets. Even when RF probabilities are truncated at the probability levels issued by the SPC, the RF forecasts often have BSSs better than or comparable to corresponding UH and SPC forecasts. Relative to the UH and SPC forecasts, the RF approach performs best for severe wind and hail prediction during the spring and summer (i.e., March–August). Overall, it is concluded that the RF method presented here provides skillful, reliable CAE-derived severe weather probabilities that may be useful to severe weather forecasters and decision-makers.

Linking weather patterns to regional extreme precipitation for highlighting potential flood events in medium‐ to long‐range forecasts

AbstractMedium‐ to long‐range precipitation forecasts are a crucial component in mitigating the impacts of fluvial flood events. Although precipitation is difficult to predict at these lead times, the forecast skill of atmospheric circulation tends to be greater. The study explores using weather patterns (WPs) as a preliminary step in producing forecasts of upper‐tail precipitation threshold exceedance probabilities for the UK. The WPs are predefined, discrete states representing daily mean sea‐level pressure (MSLP) over a European–North Atlantic domain. The WPs most likely to be associated with flooding are highlighted by calculating upper‐tail exceedance probabilities derived from the conditional distributions of regional precipitation given each WP. WPs associated with higher probabilities of extreme precipitation are shown to have occurred during two well‐known flood events: the 2014 Somerset Levels floods in southwest England; and Storm Desmond over the northern UK in December 2015. To illustrate the potential of this WP‐based prediction framework, a forecast guidance tool called Fluvial Decider is introduced. It is intended for use by hydro‐meteorologists in the England and Wales Flood Forecasting Centre (FFC). Forecasts of the MSLP from ensemble prediction systems (EPSs) are assigned to the closest‐matching WP, providing daily probabilistic forecasts of WPs out to the chosen lead time. Combining these probabilities with observed precipitation threshold exceedance probabilities provides a parsimonious tool for highlighting potential periods with increased risk of flooding. Model forecasts using the European Centre for Medium‐range Weather Forecasts (ECMWF) EPS highlighted both flood events as being at a higher than average risk of heavy extreme precipitation at lead times of over five days.

Quasi-operational forecast guidance of extremely severe cyclonic storm Fani over the Bay of Bengal using high-resolution mesoscale models

Tropical cyclone (TC) “Fani” (April 26–May 04, 2019) over the Bay of Bengal (BoB) is the first extreme severe cyclonic storm (ESCS) of the season that struck Puri coast with a maximum sustained wind speed of 110 knots. In the past 130 years, TC Fani is the first of its kind with the peculiarity of being persistent with the intensity of severe cyclonic storm (SCS) over land for more than 24 h, thus causing substantial casualties in the major cities of Odisha. Two high-resolution mesoscale modeling systems, (1) state-of-the-art advanced research version of weather research and forecasting (WRF-ARW) model with a single fixed domain (9 km horizontal resolution) and (2) TC specific hurricane WRF (HWRF) with three domains (27/9/3 km horizontal resolution) are used for real-time prediction of Fani in quasi-operational setup. Storms surge prediction is also carried out using IIT Delhi dynamical storm surge model. Both the models, WRF and HWRF, were able to predict the system from its genesis stage (5 days in advance of landfall) with reasonable accuracy in position and strength. The tracks from both models are in good agreement with the observed track. The average track errors for WRF and HWRF are 101 km and 85 km, respectively. The landfall time and position are well predicted, with 60 h of lead time. HWRF model performance in predicting landfall time, position, and intensity is significant. Along with the statistical analysis, structural and vertical analysis of wind, enthalpy flux, and thermal distribution are carried out by considering two represented initial conditions from both WRF and HWRF models. Maximum reflectivity and precipitable water diagnostic studies depict that both the models show asymmetricity at the peak intensity of ESCS. The rainfall during the landfall day from both the models is comparable with the gauge-satellite merged observed rainfall datasets. Along with TC track, intensity, and structural forecasts associated with storm surge at different vulnerable coastal cities are attempted to predict with reasonable accuracy.

A Tropical Cyclone Rapid Intensification Prediction Aid for the Joint Typhoon Warning Center’s Areas of Responsibility

Abstract In late 2017, the Rapid Intensification Prediction Aid (RIPA) was transitioned to operations at the Joint Typhoon Warning Center (JTWC). RIPA probabilistically predicts seven rapid intensification (RI) thresholds over three separate time periods: 25-, 30-, 35-, and 40-kt (1 kt ≈ 0.51 m s−1) increases in 24 h (RI25, RI30, RI35, RI40); 45- and 55-kt increases in 36 h (RI45 and RI55); and 70-kt increases in 48 h (RI70). RIPA’s probabilistic forecasts are also used to produce deterministic forecasts when probabilities exceed 40%, and the latter are included as members of the operational intensity consensus forecast aid. RIPA, initially designed for the western North Pacific, performed remarkably well in all JTWC areas of responsibility (AOR) and is now incorporated into JTWC’s ever improving suite of intensity forecast guidance. Even so, making real-time operational RIPA forecasts exposed some methodological weaknesses such as overprediction of RI for weak/disorganized systems (i.e., systems with maximum winds less than 35 kt), prediction of RI during landfall, input data reliability, and statistical inconsistencies. Modifications to the deterministic forecasts that address these issues are presented, and newly derived and more statistically consistent models are developed using data from all of JTWC’s AORs. The updated RIPA is tested as it would be run in operations and verified using a 2-yr (2018–19) independent sample. The performance from this test indicates the new RIPA—when compared to its predecessor—has improved probabilistic verification statistics, and similar deterministic skill while using fewer predictors to make forecasts.

Forecasting Peak Wind Gusts Using Meteorologically Stratified Gust Factors and MOS Guidance

Abstract Gust prediction is an important element of weather forecasting services, yet reliable methods remain elusive. Peak wind gusts estimated by the meteorologically stratified gust factor (MSGF) model were evaluated at 15 locations across the United States during 2010–17. This model couples gust factors, site-specific climatological measures of “gustiness,” with wind speed and direction forecast guidance. The model was assessed using two forms of model output statistics (MOS) guidance at forecast projections ranging from 1 to 72 h. At 11 of 15 sites the MSGF model showed skill (improvement over climatology) in predicting peak gusts out to projections of 72 h. This has important implications for operational wind forecasting because the method can be utilized at any location for which the meteorologically stratified gust factors have been determined. During particularly windy conditions the MSGF model exhibited skill in predicting peak gusts at forecast projections ranging from 6 to 72 h at roughly half of the sites analyzed. Site characteristics and local wind climatologies were shown to exert impacts on gust factor model performance. The MSGF method represents a viable option for the operational prediction of peak wind gusts, although model performance will be sensitive to the quality of the necessary wind speed and direction forecasts.

Audit firm tenure and perceived audit quality: evidence from CEO incentive contracts

PurposeThis paper aims to examine how compensation committees perceive audit quality as indicated by audit firm tenure. Using the contracting weight attached to earnings and cash flows in chief executive officer (CEO) compensation as proxy for the compensation committee’s perception of audit quality, the study examines whether compensation committees perceive performance metric informativeness as being affected by auditor tenure.Design/methodology/approachThe paper regresses CEO cash compensation on accounting-based performance metrics and on interactions between auditor tenure and accounting-based performance metrics while controlling for other factors previously shown to affect CEO pay. Auditor tenure is measured using continuous and dichotomous variables.FindingsAuditor tenure is associated with a reduced (positive) weight on earnings (operating cash flows), which suggests lower perceived audit quality as tenure lengthens consistent with the auditor closeness argument. This relation is asymmetric, i.e. the negative effect of longer auditor tenure on incentive contracting is more pronounced for positive earnings. The results are robust to using CEO total compensation as the compensation measure, as well as using level and change specifications.Research limitations/implicationsThe inability to control for audit partner tenure in assessing the effect of audit firm tenure on incentive contracting and the potential endogeneity between auditor tenure choice and incentive contracting are the main limitations of this study. Given the lack of information on US audit partner tenure, the study could not control for the audit partner tenure issue. However, the study has attempted to mitigate the endogeneity issue by using a Heckman selection model that includes in the first-stage a regression of auditor tenure on various firm, performance measure and CEO-related governance characteristics, based on existing models (Liet al., 2010).Practical implicationsCompensation committees view auditor tenure as an indicator of accounting quality in setting CEO pay. Further, long auditor tenure is perceived as detrimental to financial reporting integrity, particularly when earnings numbers suggest positive managerial performance and innovations.Originality/valueThis study provides empirical evidence that auditor tenure matters in setting executive pay. Further, this study shows evidence on the link between auditor tenure and audit quality from an internal user’s perspective. Prior studies have focused either on external users (investors, creditors) or on the preparer (using measures such as discretionary accruals or meet/beat analysts’ forecasts or forecast guidance).

Excessive Momentum and False Alarms in Late‐Spring ENSO Forecasts

AbstractThe unanticipated stalled El Niño‐Southern Oscillation (ENSO) evolution of 2014 raises questions about the reliability of the coupled models that were used for forecast guidance. Here we have analyzed the skill and reliability of forecasts of the Niño 3.4 tendency (3‐month change) in the North American multimodel ensemble (1982–2018). We found that forecasts initialized April–June (AMJ) have “excessive momentum” in the sense that the forecast Niño 3.4 tendency is more likely to be a continuation of the prior observed conditions than it should be. Models tend to predict warming when initialized after observed warming conditions and cooling when initialized after observed cooling conditions. Excessive momentum appears in AMJ forecast busts and false alarms including the 2014 one. In some models, excessive momentum appears to be related to model formulation rather than initialization. A concerning trend is that four of the nine years with AMJ forecast busts occurred in the last decade.

Sea Level Anomaly Forecasts on a Coastal Waveguide

Abstract An approach to reduce forecast data to coastal waveguide coordinates is described and demonstrated, informed by the literature on coastally trapped waves (CTWs). All discussion is limited to the Australian mainland but the approach is generally relevant to regions where CTWs influence sea level, including the Americas and Africa. The approach does not produce new forecasts, but aims to focus forecaster attention on aspects of sea level forecasts prominent on the long Australian coast. The approach also explicitly addresses spatial issues associated with measuring coastal paths. Coastal paths are scale dependent and forecast models discretize the coastal boundary differently. A well-defined coastal path is required for the quantitative application of CTW concepts such as propagation distance and offshore direction. The relevance of coastally trapped signals and remote forcing is documented in the oceanographic literature, but is effectively unknown to the general public and rarely mentioned in press reports of sea level events such as nuisance flooding. Routine presentation of forecast guidance in waveguide coordinates could contribute to the transfer of oceanographic research understanding into forecast narratives. In addition, the approach can facilitate quantitative forecast evaluations that target CTW properties. Two ocean forecast systems are contrasted in this framework for the Australian mainland. One year of daily forecasts are compared, with indications that model baroclinicity is of practical relevance.

A Statistical Model to Predict the Extratropical Transition of Tropical Cyclones

AbstractThis paper introduces a logistic regression model for the extratropical transition (ET) of tropical cyclones in the North Atlantic and the western North Pacific, using elastic net regularization to select predictors and estimate coefficients. Predictors are chosen from the 1979–2017 best track and reanalysis datasets, and verification is done against the tropical/extratropical labels in the best track data. In an independent test set, the model skillfully predicts ET at lead times up to 2 days, with latitude and sea surface temperature as its most important predictors. At a lead time of 24 h, it predicts ET with a Matthews correlation coefficient of 0.4 in the North Atlantic, and 0.6 in the western North Pacific. It identifies 80% of storms undergoing ET in the North Atlantic and 92% of those in the western North Pacific. In total, 90% of transition time errors are less than 24 h. Select examples of the model’s performance on individual storms illustrate its strengths and weaknesses. Two versions of the model are presented: an “operational model” that may provide baseline guidance for operational forecasts and a “hazard model” that can be integrated into statistical TC risk models. As instantaneous diagnostics for tropical/extratropical status, both models’ zero lead time predictions perform about as well as the widely used cyclone phase space (CPS) in the western North Pacific and better than the CPS in the North Atlantic, and predict the timings of the transitions better than CPS in both basins.

Combined Three-Stage 7-Day Weighted Analog Intensity Prediction Technique for Western North Pacific Tropical Cyclones: Demonstration of Optimum Performance

Abstract The original 7-day weighted analog intensity Pacific (WAIP) prediction technique is improved by developing a new version for the preformation stage and combining it with the bifurcation version that is especially for the intensification stage and the ending-storm stage WAIP. This combined three-stage WAIP includes a calibrated intensity spread designed to include 68% of the verifying intensities. In this demonstration of the optimum performance of the combined WAIP, the Joint Typhoon Warning Center (JTWC) best tracks, the time to formation (T2F), and ending-storm time are utilized as inputs along with the initial intensity [i.e., either 15 or 20 kt (1 kt ≈ 0.51 m s−1)]. In the new “Before Formation” (defined as 25 kt) stage of the WAIP, the intensity evolution is represented by a square function between the initial intensity and the T2F (25 kt), and very small mean absolute errors (MAEs) and intensity spreads are achieved. In the bifurcation version, correct selections between two cluster WAIP intensity evolutions lead to MAEs that slowly increase to 17 kt at 144 h, and the intensity spreads are relatively small as well. Because the ending-storm time constrains the analog selection in the WAIP, the MAEs begin to decrease after 72 h and are only 10 kt at 156 h. A case study is presented as to how an ensemble storm-track forecast along with the T2F could provide WAIP 7-day intensity predictions beginning in the preformation stage, which indicates the potential for earlier guidance for the JTWC intensity forecasts of western North Pacific tropical cyclones.

The Effects of Information Systems Compatibility on Firm Performance following Mergers and Acquisitions

ABSTRACTThis study investigates the consequences of information systems compatibility between the target and acquirer firms in the context of mergers and acquisitions (M&A). Using a unique data set of ERP implementations, we find that acquirers using the same ERP vendor as their targets exhibit shorter post-merger operating cycles and shorter post-merger audit delays relative to acquirers with different ERP vendors than their targets. In supplemental analysis, we find evidence that acquirers with the same ERP vendor as their target also exhibit more accurate management forecast guidance following the acquisition. The findings of this study should be of interest to capital market participants and managers involved in M&A activity by providing evidence about how the degree of compatibility between acquirer and target ERP systems impacts post-merger activities across different economically significant functional areas.

The Subseasonal Experiment (SubX): A Multimodel Subseasonal Prediction Experiment

AbstractThe Subseasonal Experiment (SubX) is a multimodel subseasonal prediction experiment designed around operational requirements with the goal of improving subseasonal forecasts. Seven global models have produced 17 years of retrospective (re)forecasts and more than a year of weekly real-time forecasts. The reforecasts and forecasts are archived at the Data Library of the International Research Institute for Climate and Society, Columbia University, providing a comprehensive database for research on subseasonal to seasonal predictability and predictions. The SubX models show skill for temperature and precipitation 3 weeks ahead of time in specific regions. The SubX multimodel ensemble mean is more skillful than any individual model overall. Skill in simulating the Madden–Julian oscillation (MJO) and the North Atlantic Oscillation (NAO), two sources of subseasonal predictability, is also evaluated, with skillful predictions of the MJO 4 weeks in advance and of the NAO 2 weeks in advance. SubX is also able to make useful contributions to operational forecast guidance at the Climate Prediction Center. Additionally, SubX provides information on the potential for extreme precipitation associated with tropical cyclones, which can help emergency management and aid organizations to plan for disasters.

LAMP Upgraded Convection and Total Lightning Probability and “Potential” Guidance for the Conterminous United States

Abstract Localized Aviation MOS Program (LAMP) convection and lightning probability and “potential” guidance forecasts for the conterminous United States, developed by the Meteorological Development Laboratory (MDL), have been produced operationally and made available to aviation and other users through the National Digital Guidance Database (NDGD) since April 2014. In response to user requests for improved skill and resolution of these forecasts, MDL has recently made extensive upgrades, and a switch to the new LAMP guidance was made in January 2018. Upgrades include improved spatial and temporal resolution of the predictands, which were enabled by first time LAMP use of finescale radar reflectivity products from the Multi-Radar Multi-Sensor (MRMS) system, total lightning observations from a ground-based lightning sensing system, and finescale model output from the High Resolution Rapid Refresh (HRRR) model. This article describes how these new data inputs are applied in the LAMP model to obtain improved skill and sharpness of the convection and total lightning probability forecasts. Strengths and limitations in LAMP performance are shown through verification statistics and example verification maps for a selected intense convective storm case.

An application of the LTP_DSEF model to heavy precipitation forecasts of landfalling tropical cyclones over China in 2018

Recently, a track-similarity-based Dynamical-Statistical Ensemble Forecast (LTP_DSEF) model has been developed in an attempt to predict heavy rainfall from Landfalling Tropical cyclones (LTCs). In this study, the LTP_DSEF model is applied to predicting heavy precipitation associated with 10 LTCs occurring over China in 2018. The best forecast scheme of the model with optimized parameters is obtained after testing 3452 different schemes for the 10 LTCs. Then, its performance is compared to that of three operational dynamical models. Results show that the LTP_DSEF model has advantages over the three dynamical models in predicting heavy precipitation accumulated after landfall, especially for rainfall amounts greater than 250 mm. The model also provides superior or slightly inferior heavy rainfall forecast performance for individual LTCs compared to the three dynamical models. In particular, the LTP_DSEF model can predict heavy rainfall with valuable threat scores associated with certain LTCs, which is not possible with the three dynamical models. Moreover, the model can reasonably capture the distribution of heavier accumulated rainfall, albeit with widespread coverage compared to observations. The preliminary results suggest that the LTP_DSEF model can provide useful forecast guidance for heavy accumulated rainfall of LTCs despite its limited variables included in the model.

Seasonal rainfall forecasting for the Yangtze River basin using statistical and dynamical models

AbstractSummer monsoon rainfall forecasting in the Yangtze River basin is highly valuable for water resource management and for the control of floods and droughts. However, improving the accuracy of seasonal forecasting remains a challenge. In this study, a statistical model and four dynamical global circulation models (GCMs) are applied to conduct seasonal rainfall forecasts for the Yangtze River basin. The statistical forecasts are achieved by establishing a linear regression relationship between the sea surface temperature (SST) and rainfall. The dynamical forecasts are achieved by downscaling the rainfall predicted by the four GCMs at the monthly and seasonal scales. Historical data of monthly SST and GCM hindcasts from 1982 to 2010 are used to make the forecast. The results show that the SST‐based statistical model generally outperforms the GCM simulations, with higher forecasting accuracy that extends to longer lead times of up to 12 months. The SST statistical model achieves a correlation coefficient up to 0.75 and the lowest mean relative error of 6%. In contrast, the GCMs exhibit a sharply decreasing forecast accuracy with lead times longer than 1 month. Accordingly, the SST statistical model can provide reliable guidance for the seasonal rainfall forecasts in the Yangtze River basin, while the results of GCM simulations could serve as a reference for shorter lead times. Extensive scope exists for further improving the rainfall forecasting accuracy of GCM simulations.

Role of Sea Surface Temperature in Modulating Life Cycle of Tropical Cyclones over Bay of Bengal

Sea surface temperature (SST) varies significantly in the presence of tropical cyclones (TCs). Using fixed SST throughout the integration of high resolution TC models is general practice in research and operational endeavor over the North Indian Ocean. The present study is to assess the impact of updating realistic SST in TC lifetime on track, intensity and rainfall of TCs. The Hurricane Weather Research Forecast (HWRF) model of single domain with 9km resolution is used. A total of 31 forecast cases are considered from 6 TCs during 2007- 16 with unique track and intensity characteristics. A set of two numerical experiments are done without (CNTL) and with 6-hourly SST update (SST) in TC lifetime.Mean track and intensity errors show that there is an improvement of 3–41% in track during 12–120h forecast length for SST run. The SST runs improve landfall position and time prediction by 20% and 33% respectively. The cross track error of SST run is comparable (44km) with average errors available for this basin (34km); and along track errors are improved by 60% as compared to CNTL as well as average errors of the basin. The model is biased to overestimate a weaker TC and underestimate a stronger TC, however, the bias is reduced in SST run by 5–51%. The analyses of wind, enthalpy flux and warm core structures provide insight for realistic intensity prediction of SST run unlike CNTL. Rainfall intensity and radial distribution is also improved in SST run. Thus, this study highlights the significance of ocean coupling with TC models to advance forecast guidance.