Global Forecast System Forecasts Research Articles

Why Was the Weakening of Typhoon In-fa (2021) to the East of Taiwan Island Not Forecasted in the GFS?

Abstract Typhoon In-fa (2021) experienced a weakening process on 22–23 July in a large-scale environment favorable for tropical cyclone (TC) intensification. All operational forecasts and the Global Forecast System (GFS) forecasts predicted a continuous intensification, which deviated significantly from the observation. The analysis of the GFS analysis product shows a coalescence process of Typhoon In-fa with an intraseasonal monsoon gyre during the period, resulting in an increased outer size of In-fa and well-organized convection to the east, which prevented transporting the mass and moisture into the inner-core area of In-fa, thus leading to the weakening. Nevertheless, this essential coalescence process was not captured in the GFS forecasts due to the poor prediction of the monsoon gyre. The analysis shows that the forecasted monsoon gyre on 20–22 July had an eastward location at 72- and 96-h lead times and a weaker intensity and outer circulation at 24- and 48-h lead times, leading to the forecasted TC always moving in its north and west, in agreement with numerical simulation results that the monsoon gyre with a weaker outer circulation is not conducive to the coalescence. Thus, the deep convection to the east of In-fa preventing the inward transportation of mass and moisture did not develop in the GFS forecasts. As a result, the GFS forecasted that In-fa would continue intensifying in a favorable environment on 22–23 July. The findings of this study would prompt forecasters to pay attention to the prediction of the monsoon gyre and its influence on the TC intensity in forecast products available to them.

Displacement Error Characteristics of 500-hPa Cutoff Lows in Operational GFS Forecasts

Abstract Cutoff lows are often associated with high-impact weather; therefore, it is critical that operational numerical weather prediction systems accurately represent the evolution of these features. However, medium-range forecasts of upper-level features using the Global Forecast System (GFS) are often subjectively characterized by excessive synoptic progressiveness, i.e., a tendency to advance troughs and cutoff lows too quickly downstream. To better understand synoptic progressiveness errors, this research quantifies seven years of 500-hPa cutoff low position errors over the globe, with the goal of objectively identifying regions where synoptic progressiveness errors are common and how frequently these errors occur. Specifically, 500-hPa features are identified and tracked in 0–240-h 0.25° GFS forecasts during April 2015–March 2022 using an objective cutoff low and trough identification scheme and compared to corresponding 500-hPa GFS analyses. In the Northern Hemisphere, cutoff lows are generally underrepresented in forecasts compared to verifying analyses, particularly over continental midlatitude regions. Features identified in short- to long-range forecasts are generally associated with eastward zonal position errors over the conterminous United States and northern Asia, particularly during the spring and autumn. Similarly, cutoff lows over the Southern Hemisphere midlatitudes are characterized by an eastward displacement bias during all seasons. Significance Statement Cutoff lows are often associated with high-impact weather, including excessive rainfall, winter storms, and severe weather. GFS forecasts of cutoff lows over the United States are often subjectively noted to advance cutoff lows too quickly downstream, and thus limit forecast skill in potentially impactful scenarios. Therefore, this study quantifies the position error characteristics of cutoff lows in recent GFS forecasts. Consistent with typically anecdotal impressions of cutoff low position errors, this analysis demonstrates that cutoff lows over North America and central Asia are generally associated with an eastward position bias in medium- to long-range GFS forecasts. These results suggest that additional research to identify both environmental conditions and potential model deficiencies that may exacerbate this eastward bias would be beneficial.

Evaluation of the SACZ index as a prognostic tool based on GFS forecasts

The South Atlantic Convergence Zone (SACZ) is an atmospheric phenomenon typical of summertime where a band of nebulosity causes intense or persistent rainfall in many regions of Brazil. SACZ episodes can be responsible for many natural disasters. Besides, the impacts of rainfall on water availability and consequently on the energy sector are extensive. The main objective of this study was to investigate the implementation of the SACZ index as an objective forecasting tool using input data from the Global Forecast System (GFS) model. Initially, we compared the index with the SACZ events identified by the Center for Weather Forecasting and Climate Studies (CPTEC ) from 2017 to 2021. Results showed that the index represented all events identified SACZs by CPTEC. Finally, we used data from the GFS 0.25 Degree from 2017 to 2021 to calculate Accuracy, Probability of detection, and False alarm ratio to evaluate the SACZ index as a prediction tool. Three thresholds are defined for the binary classification of a possible SACZ event. Results showed that above the most sensitive threshold (h1), within 10 days in advance, the sign of a possible SACZ can be detected. For the intermediate threshold (h2), a forecast of 96 h can detect a sign. For the most specific threshold (h3), the index can detect the event within 72 h in advance with a probability of detection of almost 90%. The SACZ index proved to be an efficient tool for detecting the dynamics of the phenomenon and can be used to assist operationally and in decision-making.

Flood Predictability of One-Way and Two-Way WRF Nesting Coupled Hydrometeorological Flow Simulations in a Transboundary Chenab River Basin, Pakistan

Remote sensing precipitation or precipitation from numerical weather prediction (NWP) is considered to be the best substitute for in situ ground observations for flood simulations in transboundary, data-scarce catchments. This research was aimed to evaluate the possibility of using a combination of a satellite precipitation product and NWP precipitation for better flood forecasting in the transboundary Chenab River Basin (CRB) in Pakistan. The gauge-calibrated satellite precipitation product, i.e., Global Satellite Mapping of Precipitation (GSMaP_Gauge), was selected to calibrate the Integrated Flood Analysis System (IFAS) model for the 2016 flood event in the Chenab River at the Marala Barrage gauging site in Pakistan. Precipitation from the Global Forecast System (GFS) NWP, with nine different lead times up to 4 days, was used in the calibrated IFAS model to predict the flood hydrograph in the Chenab River. The hydrologic simulations, with global GFS forecasts, were unable to predict the flood peak for all lead times. Then, the Weather Research and Forecasting (WRF) model was used to downscale the precipitation forecasts with one-way and two-way nesting approaches. In the WRF model, the CRB was centered in two domains of 25 km and 5 km resolutions. The downscaled precipitation forecasts were subsequently supplied to the IFAS model, and the predicted simulations were compared to obtain the optimal flood peak simulation in the Chenab River. It was found in this study that the simulated hydrographs, at different lead times, from the precipitation of two-way WRF nesting exhibited superior performance with the highest R2 and Nash–Sutcliffe efficiency (NSE) and the lowest percent bias (PBIAS) compared with one-way nesting. Moreover, it was concluded that the combination of GFS forecast and two-way WRF nesting can provide high-quality precipitation prediction to simulate flood hydrographs with a remarkable lead time of 96 h when applying coupled hydrometeorological flow simulation.

Improvement of displacement error of rainfall and wind field forecast associated with landfalling tropical cyclone AMPHAN

Spatial distribution of rainfall and wind speed forecast errors associated with landfalling tropical cyclones (TC) occur significantly due to incorrect location forecast by numerical models. Two major areas of errors are: (i) over-estimation over the model forecast locations and (ii) underestimation over the observed locations of the TCs. A modification method is proposed for real-time improvement of rainfall and wind field forecasts and demonstrated for the typical TC AMPHAN over the Bay of Bengal in 2020. The proposed method to improve the model forecasts is a relocation method through shifting of model forecast locations of TC to the real-time official forecast locations of India Meteorological Department (IMD). The modification is applied to the forecasts obtained from the operational numerical model, the Global Forecast System (GFS) of IMD. Application of the proposed method shows considerable improvement of both the parameters over both the locations. The rainfall forecast errors due to displacement are found to have improved by 44.1%–69.8% and 72.1%–85.2% over the GFS forecast locations and over the observed locations respectively for the respective forecast lead times 48 h, 72 h, and 96 h. Similarly, the wind speed forecasts have improved by 27.6%–56.0% and 63.7%–84.6% over the GFS forecast locations and over the observed locations respectively for the respective forecast lead times 60 h, 72 h, and 84 h. The results show that the proposed technique has capacity to provide improved spatial distributions of rainfall and wind speed forecasts associated with landfalling TCs and useful guidance to operational forecasters.

The Skills of Medium-Range Precipitation Forecasts in the Senegal River Basin

Reliable information on medium-range (1–15 day) precipitation forecasts is useful in reservoir operation, among many other applications. Such forecasts are increasingly becoming available from global models. The skills of medium-range precipitation forecasts derived from Global Forecast System (GFS) are assessed in the Senegal River Basin, focusing on the watershed its major hydropower dams: Manantali (located in relatively wet, Southern Sudan climate and mountainous region), Foum Gleita (relatively dry, Sahel climate and low-elevation), and Diama (a large watershed covering almost the entire basin, dominated by Sahel climate). IMERG Final, a satellite product involving rain gauge data for bias correction, is used as reference. GFS has the ability capture the overall spatial and monthly pattern of rainfall in the region. However, GFS tends to overestimate rainfall in the wet parts of the region, and slightly underestimate in the dry part. The skill of daily GFS forecast is low over Manantali (Kling-Gupta Efficiency, KGE of 0.29), but slightly higher over Foum Gleita (KGE of 0.53) and Diama (KGE of 0.59). For 15-day accumulation, GFS forecast shows higher skill over Manantali (KGE of 0.60) and Diama (KGE of 0.79) but does not change much over Foul Gleita (KGE of 0.51) compared to daily rainfall forecasts. IMERG Early, a satellite-only product available at near-real time, has better performance than GFS. This study suggests the need for further improving the accuracy of GFS forecasts, and identifies IMERG Early as a potential source of data that can help in this effort.

Tropical cyclone track and intensity prediction skill of GFS model over NIO during 2019 & 2020

The Tropical Cyclone (TC) track prediction using different NWP models and its verification is the critical task to provide prior knowledge about the model errors, which is beneficial for giving the model guidance-based real-time cyclone warning advisories. This study has attempted to verify the Global Forecast System (GFS) model forecasted tropical cyclone track and intensity over the North Indian Ocean (NIO) for the years 2019 and 2020. GFS is one of the operational models in the India Meteorological Department (IMD), which provides the medium-range weather forecast up to 10 days. The forecasted tracks from the GFS forecast are obtained using a vortex tracker developed by Geophysical Fluid Dynamics Laboratory (GFDL). A total of 13 tropical cyclones formed over the North Indian Ocean, eight during 2019 and five in 2020 have been considered in this study. The accuracy of the model predicted tracks and intensity is verified for five days forecasts (120 h) at 6-h intervals; the track errors are verified in terms of Direct Position Error (DPE), Along Track Error (ATE) and Cross-Track Error (CTE). The annual mean DPE over NIO during 2019 (51–331 km) is lower than 2020 (82–359 km), and the DPE is less than 150 km up to 66 h during 2019 and 48 h during 2020. The positive ATE (76–332 km) indicates the predicted track movement is faster than the observed track during both years. The positive CTE values for most forecast lead times suggest that the predicted track is towards the right side of the observed track during both years. The cyclone Intensity forecast for the maximum sustained wind speed (MaxWS) and central mean sea level pressure (MSLP) are verified in terms of mean error (ME) and root mean square error (RMSE). The errors are lead time independent. However, most of the time model under-predicted the cyclone intensity during both years. Finally, there is a significant variance in track and intensity errors from the cyclone to cyclone and Bay of Bengal basin to the Arabian Sea basin.

The Accuracy of Precipitation Forecasts at Timescales of 1–15 Days in the Volta River Basin

Medium-range (1–15 day) precipitation forecasts are increasingly available from global weather models. This study presents evaluation of the Global Forecast System (GFS) for the Volta river basin in West Africa. The evaluation was performed using two satellite-gauge merged products: NASA’s Integrated Multi-satellitE Retrievals (IMERG) “Final Run” satellite-gauge merged rainfall observations, and the University of California Santa Barbara’s Climate Hazard’s group Infrared Precipitation with Stations (CHIRPS). The performance of GFS depends on the climate zone, with underestimation bias in the dry Sahel climate, overestimation bias in the wet Guinea Coastal climate, and relatively no bias in the moderately wet Savannah climate. Averaging rainfall over the watershed of the Akosombo dam (i.e., averaging across all three climate zones), the GFS forecast indicates low skill (Kling-Gupta Efficiency KGE = 0.42 to 0.48) for the daily, 1-day, lead GFS forecast, which deteriorates further as the lead time increases. A sharp decrease in KGE occurred between 6 to 10 days. Aggregating the forecasts over long timescales improves the accuracy of the GFS forecasts. On a 15-day accumulation timescale, GFS shows higher skills (KGE = 0.74 to 0.88).

Performance of the Global Forecast System's medium-range precipitation forecasts in the Niger river basin using multiple satellite-based products

Abstract. Accurate weather forecast information has the potential to improve water resources management, energy, and agriculture. This study evaluates the accuracy of medium-range (1–15 d) precipitation forecasts from the Global Forecast System (GFS) over watersheds of eight major dams (Selingue Dam, Markala Dam, Goronyo Dam, Bakolori Dam, Kainji Dam, Jebba Dam, Dadin Kowa Dam, and Lagdo Dam) in the Niger river basin using NASA's Integrated Multi-satellitE Retrievals (IMERG) Final Run merged satellite gauge rainfall observations. The results indicate that the accuracy of GFS forecast varies depending on climatic regime, lead time, accumulation timescale, and spatial scale. The GFS forecast has large overestimation bias in the Guinea region of the basin (wet climatic regime), moderate overestimation bias in the Savannah region (moderately wet climatic regime), but has no bias in the Sahel region (dry climate). Averaging the forecasts at coarser spatial scales leads to increased forecast accuracy. For daily rainfall forecasts, the performance of GFS is very low for almost all watersheds, except for Markala and Kainji dams, both of which have much larger watershed areas compared to the other watersheds. Averaging the forecasts at longer timescales also leads to increased forecast accuracy. The GFS forecasts, at 15 d accumulation timescale, have better performance but tend to overestimate high rain rates. Additionally, the performance assessment of two other satellite products was conducted using IMERG Final estimates as reference. The Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) merged satellite gauge product has similar rainfall characteristics to IMERG Final, indicating the robustness of IMERG Final. The IMERG Early Run satellite-only rainfall product is biased in the dry Sahel region; however, in the wet Guinea and Savannah regions, IMERG Early Run outperforms GFS in terms of bias.

Evaluation of Global Forecast System (GFS) Medium-Range Precipitation Forecasts in the Nile River Basin

Abstract Reliable weather forecasts are valuable in a number of applications, such as agriculture, hydropower, and weather-related disease outbreaks. Global weather forecasts are widely used, but detailed evaluation over specific regions is paramount for users and operational centers to enhance the usability of forecasts and improve their accuracy. This study presents evaluation of the Global Forecast System (GFS) medium-range (1–15 day) precipitation forecasts in the nine subbasins of the Nile basin using NASA’s Integrated Multisatellite Retrievals (IMERG) Final Run satellite–gauge merged rainfall observations. The GFS products are available at a temporal resolution of 3–6 h and a spatial resolution of 0.25°, and the version-15 products are available since 12 June 2019. GFS forecasts are evaluated at a temporal scale of 1–15 days, a spatial scale from 0.25° to all the way to the subbasin scale, and for a period of one year (15 June 2019–15 June 2020). The results show that performance of the 1-day lead daily basin-averaged GFS forecast performance, as measured through the modified Kling–Gupta efficiency (KGE), is poor (0 < KGE < 0.5) for most of the subbasins. The factors contributing to the low performance are 1) large overestimation bias in watersheds located in wet climate regimes in the northern hemispheres (Millennium watershed, Upper Atbara and Setit watershed, and Khashm El Gibra watershed), and 2) lower ability in capturing the temporal dynamics of watershed-averaged rainfall that have smaller watershed areas (Roseires at 14 110 km2 and Sennar at 13 895 km2). GFS has better bias for watersheds located in the dry parts of the Northern Hemisphere or wet parts of the Southern Hemisphere, and better ability in capturing the temporal dynamics of watershed-average rainfall for large watershed areas. IMERG Early has better bias than GFS forecast for the Millennium watershed but still comparable and worse bias for the Upper Atbara and Setit and Khashm El Gibra watersheds. The variation in the performance of the IMERG Early could be partly explained by the number of rain gauges used in the reference IMERG Final product, as 16 rain gauges were used for the Millennium watershed but only one rain gauge over each Upper Atbara and Setit and Khashm El Gibra watershed. A simple climatological bias correction of IMERG Early reduces in the bias in IMERG Early over most watersheds, but not all watersheds. We recommend exploring methods to increase the performance of GFS forecasts, including postprocessing techniques through the use of both near-real-time and research-version satellite rainfall products.

Evaluation of Three Numerical Weather Prediction Models for the Weddell Sea Region for the Austral Winter 2013

AbstractIt is widely recognized that numerical weather prediction (NWP) results for the Antarctic are relatively poor compared to the mid‐latitudes. In this study, we evaluate output from three operational NWP systems: the ECMWF, Global Forecast System (GFS) and Antarctic Mesoscale Prediction System (AMPS), for the Austral winter (June‐August) of 2013 for the Weddell Sea region, paying special attention to regional patterns of error statistics. This is the first evaluation of NWP systems over the Southern Ocean that also addresses the accuracy of forecasted vertical profiles. In the evaluation, we use data from land‐ and ship‐based automatic weather stations (AWS) and radiosoundings. While the ECMWF and AMPS forecasts are on average biased cold and dry near the surface, the GFS forecasts are on average biased warm and moist. The near‐surface wind speed is on average overestimated by the AMPS forecasts, whereas it is slightly underestimated by the forecasts of the other two NWP systems. Among the variables investigated, all three NWP systems forecast the near‐surface specific humidity most accurately, followed by the temperature and then the wind speed. The forecast quality for the near‐surface and upper‐air wind speed degrades the most rapidly with increasing lead time, compared to the other variables. ECMWF is the overall best NWP system when compared against both the near‐surface and upper‐air observations, followed by AMPS and then GFS. The generally poorest model performance is found in locations with complex terrain along the coast of the Antarctic continent, and the best over the ocean.

A comparison of statistical and dynamical downscaling methods for short‐term weather forecasts in the US Northeast

AbstractThe Weather Research and Forecasting model (WRF) was used to produce both 9 and 3 km resolution ensemble forecasts from the deterministic Global Forecast System (GFS) model for microclimatic, agricultural regions in New York State. The forecasts were then statistically post‐processed to generate probabilistic forecasts for surface temperature (T), specific humidity (q), incoming solar radiation (SR) and precipitation (P). T was post‐processed with non‐homogeneous Gaussian regression (NGR), q and SR with truncated NGR, and P with extended logistic regression. A comparison of forecast skill was conducted between these post‐processed forecasts, the raw WRF output, the GFS forecasts and forecasts from the National Weather Service's deterministic National Digital Forecast Database (NDFD). Overall, significant improvement was observed in post‐processed WRF forecasts over all other methods for all locations and variables. Furthermore, raw WRF ensembles were found to outperform deterministic NDFD, so that if observational data are unavailable for post‐processing, dynamically downscaled WRF should be selected over deterministic, human‐altered NDFD forecasts. Finally, the 9 km post‐processed WRF had the same forecast skill as the 3 km post‐processed WRF, except for precipitation, rendering the 3 km WRF unnecessary if observational data are available, saving computational cost.

Predictability of PM2.5 in Seoul based on atmospheric blocking forecasts using the NCEP global forecast system

We investigate the predictability of PM2.5 (particulate matter with diameters ≤ 2.5 μm) concentration in Seoul using atmospheric blocking forecasts during four winter seasons (December, January, and February) of 2015–2019. Atmospheric blocking is predicted using the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS), and PM2.5 concentration in Seoul of the same period are obtained from the surface measurements of the National Institute of Environmental Research (NIER).We define blocking indices using two methods: the anomaly method and the gradient method. Correlation analysis establishes the relationship between the blocking and PM2.5 concentrations in Seoul. The result based on the anomaly method exhibits strong positive and negative correlation coefficients respectively over the East Sea and Japan and the vicinity of Lake Baikal. The correlation coefficient from the blocking index of the gradient method reveals a similar pattern, and the maximum values are found within the longitude range of 130–150°E Building on the relationship between PM2.5 in Seoul and the blocking index, we perform PM2.5 prediction using the GFS forecast of the atmospheric blocking index. The prediction shows reliable performance at least up to a five-day forecast. This atmospheric blocking based dynamical and statistical PM2.5 prediction can be a useful tool for mid-range PM2.5 forecast.

Examination of WRF-ARW Experiments Using Different Planetary Boundary Layer Parameterizations to Study the Rapid Intensification and Trajectory of Hurricane Otto (2016)

Hurricane Otto (2016) was characterised by remarkable meteorological features of relevance for the scientific community and society. Scientifically, among the most important attributes of Otto is that it underwent a rapid intensification (RI) process. For society, this cyclone severely impacted Costa Rica and Nicaragua, leaving enormous economic losses and many fatalities. In this study, a set of three numerical simulations are performed to examine the skill of model estimations in reproducing RI and trajectory of Hurricane Otto by comparing the results of a global model to a regional model using three different planetary boundary layer parameterizations (PBL). The objective is to set the basis for future studies that analyse the physical reasons why a particular simulation (associated with a certain model setup) performs better than others in terms of reproducing RI and trajectory. We use the regional model Weather Research and Forecasting—Advanced Research WRF (WRF-ARW) with boundary and initial conditions provided by the Global Forecast System (GFS) analysis (horizontal resolution of 0.5 degrees). The PBL used are the Medium Range Forecast, the Mellor-Yamada-Janjic (MYJ), and the Yonsei University (YSU) parameterizations. The regional model is run in three static domains with horizontal grid spacing of 27, 9 and 3 km, the latter covering the spacial extent of Otto during the simulation period. WRF-ARW results improve the GFS forecast, in almost every aspect evaluated in this study, particularly, the simulated trajectories in WRF-ARW show a better representation of the cyclone path and movement compared to GFS. Even though the MYJ experiment was the only one that exhibited an abrupt 24-h change in the storm’s surface wind, close to the 25-knot threshold, the YSU scheme presented the fastest intensification, closest to reality.

Role of SST feedback in the prediction of the boreal summer monsoon intraseasonal oscillation

This study investigates the impact of different specification of the underlying sea surface temperature (SST) on the prediction of intraseasonal rainfall variation associated with strong Monsoon Intraseasonal Oscillation (MISO) events in the northern Indian Ocean. A series of forecast experiments forced with observed hourly, daily, or seasonal SSTs are performed for three selected strong MISO events using the National Centers for Environmental Predictions (NCEP) atmospheric Global Forecast System (GFS). The comparison between these GFS forecasts shows that the intraseasonal SST variability is more important than its diurnal variability in the MISO prediction. The GFS experiments forced with daily SST which includes intraseasonal variability has higher prediction skill and faster speed in the northward propagation of the MISO intraseasonal rainfall anomalies than those forced with seasonal SST that do not include intraseasonal variability. No significant difference is found in the MISO prediction when GFS was forced by SST with or without SST diurnal cycle. The GFS runs forced with warmer and colder seasonal SSTs which mimic possible biases in SST prediction have comparable skill in the MISO prediction. A modified version of the NCEP Climate Forecast System coupled model (CFSm5) with 1- and 10-m vertical resolutions in the upper ocean is then used to examine their performance in the MISO prediction when all aspects of SST are actively included. The CFSm5 with 1-m vertical resolution in the upper ocean (CFSm501) shows larger amplitude of intraseasonal SST anomaly, with higher prediction skill in both intraseasonal SST and rainfall than the CFSm5 with the typical 10-m vertical resolution in the upper ocean (CFSm510) does. Compared with the uncoupled GFS, both CFSm501 and CFSm510, despite errors in predicted SSTs, have better prediction skill and more reasonable rainfall variability, which is attributed to the inclusion of active air–sea interaction. These results suggest the importance of intraseasonal variability of SST and air–sea interaction in improving the intraseasonal rainfall prediction associated with the MISO.

Sub-optimal model-based deficit irrigation scheduling with realistic weather forecasts

This paper analyses the performance of sub-optimal irrigation schedules obtained daily by solving a multi-objective optimization problem with updated weather measurements and forecasts. The approach was tested using five crops at four European locations with contrasting weather conditions. Four- and 6-day Global Forecast System (GFS) forecasts were used at all locations, and comparison with a down-scaled locally tuned model was conducted at one location. Accurate GFS temperature forecasts were observed at all four locations, but the accuracy of the potential evapotranspiration calculated from the GFS forecasts was not as consistent. Precipitations forecasts were very poor at all locations. In Greece, the down-scaled locally tuned forecasts were only marginally better than the GFS ones. In most cases, recomputing the sub-optimal irrigation schedule daily greatly reduced the impact of the imperfect weather forecasts on the final results. Using 4- or 6-day actual forecasts did not yield results appreciably better than those obtained using only historical averages as surrogate forecasts. The main consequence of the imperfect forecasts was that the final yield differed from the target one, but the (yield, irrigation) combination remained close to optimal, unless the target yield was set too high and water availability was not the main factor limiting crop development.

R2O Transition of NCAR’s Icing and Turbulence Algorithms into NCEP’s Operations

National Center for Environmental Prediction (NCEP) started distributing global operational gridded in flight icing, turbulence and convective cloud products as part of World Area Forecast System (WAFS) products in 2007. Simple algorithms were used to derive these products during early stage based on NCEP Global Forecast System (GFS) forecast. These products quickly became essential flight planning tool for international aviation community and are especially important to developing countries that do not have resource to run numerical models themselves. To further improve these products, Environmental Modeling Center (EMC) started collaborating with National Center for Atmospheric Research (NCAR) to transition their aviation research algorithms into NCEP’s operations (R2O), particularly Forecast Icing Potential (FIP) and Graphical Turbulence Guidance (GTG) algorithms. The initial attempt is to apply FIP to GFS forecast to potentially replace WAFS icing product. Extensive evaluation demonstrated FIP outperformed original WAFS icing product and, with support from Aviation Weather Center (AWC) and Federal Aviation Administration (FAA), EMC replaced US WAFS icing product with FIP in 2015. EMC recently also implemented GTG with 2017 GFS upgrade but GTG will not replace WAFS turbulence until 2019. This paper will describe the methodology which EMC used to transition NCAR’s aviation research algorithms into NCEP’s operations. It will also describe how EMC generates icing analysis data to be used as truth for performing objective verification. Several case studies will be presented and the methodology and results for objective validation will be discussed. Finally, future collaboration plan with NCAR and implementation plans to continue to improve WAFS products will be stated.

Real-time Extremely Heavy Rainfall Forecast and Warning over Rajasthan During the Monsoon Season (2016)

Two events of extremely heavy rainfall occurred over Rajasthan during 7–9 August 2016 and 19–21 August 2016. Due to these events, flooding occurred over east Rajasthan and affected the normal life of people. A low-pressure area lying over northwest Madhya Pradesh on 7 August 2016 moved north-westward and lay near east Rajasthan and adjoining northwest Madhya Pradesh on 8 and 9 August 2016. Under the influence of this low-pressure system, Chittorgarh district and adjoining areas of Rajasthan received extremely heavy rainfall of 23 cm till 0300 UTC of 8 August 2016 and 34 cm on 0300 UTC of 9 August 2016. A deep depression lying over extreme south Uttar Pradesh and adjoining northeast Madhya Pradesh on 19 August 2016 moved westward and gradually weakened into a depression on 20 August 2016. It further weakened into a low-pressure area and lay over east Rajasthan on 21 and 22 August 2016. Under the influence of this deep depression, Jhalawar received 31 cm and Baran received 25 cm on 19 August. On 20 August 2016, extremely heavy rainfall (EHR) occurred over Banswara (23.5 cm), Pratapgarh (23.2 cm) and Chittorgarh (22.7 cm) districts. In this paper, the performance of the National Centers for Environmental Prediction (NCEP) global forecast system (GFS) model for real-time forecast and warning of heavy to very heavy/EHR that occurred over Rajasthan during 7–9 August 2016 and 19–21 August 2016 has been examined. The NCEP GFS forecast rainfall (Day 1, Day 2 and Day 3) was compared with the corresponding observed gridded rainfall. Based on the predictions given by the NCEP GFS model for heavy rainfall and with their application in real-time rainfall forecast and warnings issued by the Regional Weather Forecasting Center in New Delhi, it is concluded that the model has predicted the wind pattern and EHR event associated with the low-pressure system very accurately on day 1 and day 2 forecasts and with small errors in intensity and space for day 3.

Forecast Advisory for a Cold-Season Heavy Rainfall/Flood Event That Developed from Multiple Interactions of the Cold-Surge Vortex with Cold-Surge Flows in the South China Sea

Abstract The peak intensity occurrence frequency over the life cycles of parent cold-surge vortices (CSVs) for heavy rainfall/flood (HRF) events is classified into two types depending on their life cycles having two or three peak intensities, denoted as HRF2 or HRF3, respectively. The formation of an HRF2 event from its parent CSV(HRF2) formation is ≤5 days, while the formation of an HRF3 event is ≥6 days. The latter group contributes ~57% of the total number of HRF events. As a result of some model constraints, the formation and development of HRF3 events are not well forecasted by the Global Forecast System (GFS) and regional forecast models. The life cycle and second peak intensity for CSV(HRF3) allow for the introduction of a forecast advisory for HRF3 events. Identification of CSVs and two sufficient requirements for the formation and occurrence of HRF events were developed by previous studies. Nevertheless, two new necessary steps are now included in the proposed forecast advisory. The population ratio for CSV(HRF3) and the regular CSV is only about 15%. The occurrence optimum time to for the CSV(HRF3) second peak intensity from this vortex formation is about 3 days 6 h. The GFS forecast over to is utilized to identify CSV(HRF3). Then, the relay of the GFS forecast from the occurrence time of the CSV(HRF3) second peak is used to predict the formation/occurrence of HRF3 events. Six HRF3 events during cold seasons for 2013–16 are used to test the feasibility of this forecast advisory. Results clearly demonstrate this advisory is a success for the forecast of HRF3 events over the entire life cycles of their parent CSV(HRF3)s.

Analysis of an Observing System Experiment for the Joint Polar Satellite System

Abstract The Joint Polar Satellite System (JPSS) is a key contributor to the next-generation operational polar-orbiting satellite observing system. In the JPSS era, the complete polar-orbiting observing system will be composed of two satellites—in the midmorning (mid-AM) and afternoon (PM) orbits—each with thermodynamic sounding capabilities from both microwave and hyperspectral infrared instruments. JPSS will occupy the PM orbit, while the Meteorological Operational (MetOp) system, sponsored by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT), will occupy the mid-AM orbit. While the current polar-orbiting satellite system has been thoroughly evaluated, information about its resilience and efficacy in the JPSS era is needed. A 7-month (August 2012–February 2013) observing system experiment (OSE) was run with the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS). Observations were selected from operational satellite data platforms to be representative of the polar-orbiting data in the JPSS era. Overall, removing data from the PM orbit produced inferior scores, with the impact greater in the Southern Hemisphere (SH) than in either the Northern Hemisphere (NH) or the tropics. For the entire 7 months, the time-mean 500-hPa geopotential height anomaly correlation (Z500AC) decreased by 0.005 and 0.013 in the NH and SH, respectively—both of which are statistically significant at the 95% level. Additionally, a detailed statistical analysis of the distribution of Z500AC skill scores is presented and compared with historical accuracy data. It was determined that eliminating PM orbit data resulted in a higher probability of producing low scores and a lower probability of producing high scores, counter to the trend in GFS forecast skill over the last 20 years.