Central Pressure Error Research Articles

Optimizing the Assimilation of the GOES-16/-17 Atmospheric Motion Vectors in the Hurricane Weather Forecasting (HWRF) Model

Hourly and 15 min GOES-16 and -17 atmospheric motion vectors (AMVs) are evaluated using the 2020 version of the operational HWRF to assess their impact on tropical cyclone forecasting. The evaluation includes infrared (IR), visible (VIS), shortwave (SWIR), clear air, and cloud top water vapor (CAWV and CTWV) AMVs derived from the ABI imagery. Several changes are made to optimize the assimilation of these winds. The observational error profile is inflated to avoid overweighting of the AMVs. The range of allowable AMV wind speeds entering the assimilation system is increased to include larger wind speeds observed in tropical cyclones. Two data quality checks, commonly used for rejecting AMVs, namely QI and PCT1, have been removed. These changes resulted in a 20–40% increase in the number of AMVs assimilated. One additional change, specific to infrared AMVs, is narrowing the atmospheric layer where IR AMVs are rejected from 400–800 hPa to 400–600 hPa. The AMVs’ impact on forecast skill is assessed using storms from the North Atlantic and the Eastern Pacific, respectively. Overall, GOES-16 and -17 AMVs are beneficial for improving tropical cyclone forecasting. Positive analysis and forecast impact are obtained for track error, intensity error, minimum central pressure error, and storm size.

Evaluating Benefits of Two-Way Ocean–Atmosphere Coupling for Global NWP Forecasts

AbstractWe evaluate the impact of adding two-way coupling between atmosphere and ocean to the Met Office deterministic global forecast model. As part of preoperational testing of this coupled NWP configuration we have three years of daily forecasts, run in parallel to the uncoupled operational forecasts. Skill in the middle and upper troposphere out to T + 168 h is generally increased compared to the uncoupled model. Improvements are strongest in the tropics and largely neutral in midlatitudes. We attribute the additional skill in the atmosphere to the ability of the coupled model to predict sea surface temperature (SST) variability in the (sub)tropics with greater skill than persisted SSTs as used in uncoupled forecasts. In the midlatitude, ocean skill for SST is currently marginally worse than persistence, possibly explaining why there is no additional skill for the atmosphere in midlatitudes. Sea ice is predicted more skillfully than persistence out to day 7 but the impact of this on skill in the atmosphere is difficult to verify. Two-way air–sea coupling benefits tropical cyclone forecasts by reducing median track and central pressure errors by around 5%, predominantly from T + 90 to T + 132 h. Benefits from coupling are largest for large cyclones, and for smaller storms coupling can be detrimental. In this study skill in forecasts of the Madden–Julian oscillation does not change with two-way air–sea coupling out to T + 168 h.

Assessment of NCMRWF Global Ensemble System with differing ensemble populations for Tropical cyclone prediction

Ensemble Prediction System (EPS) estimates the probability distribution of atmospheric states through multiple integrations of the Numerical Weather Prediction (NWP) model/s with number of perturbed initial conditions. The choice of an ensemble size can affect the skill of the ensemble forecast. Since more ensemble members require more computational requirements, it is important to determine whether the larger ensemble size significantly improves the forecast uncertainty estimates. The EPS at National Centre for Medium Range Weather Forecasting (NEPS) is assessed for the prediction of track and intensity of Tropical Cyclones (TC) over Bay of Bengal (BoB). Medium range forecast of four tropical cyclones over BoB with three different ensemble sizes namely E11 (11 perturbed members), E22 (22 perturbed members) and E44 (44 perturbed members) are presented here. The results of the study indicate that doubling of the ensemble population reduced the mean initial position errors by 25% and increase of ensemble size by four times (E11 to E44) reduced the errors by about 38%. The ensemble mean forecast track has lower errors compared to the deterministic forecast (CNTL) at shorter lead times for all ensemble sizes. The impact of ensemble size is significant on the improvement of mean track prediction skill at longer lead times. Median of central pressure error is very close to zero for all the sizes and the forecasts are slightly underestimated compared to the observed values. The initial error in Maximum Sustained surface Wind speed (MSW) is smaller for E44 as compared to E22 and E11. Interestingly, in all lead time forecasts and all the ensemble sizes, ensemble mean MSW errors are nearly within ±5 ms−1. There is only slight improvement in the ensemble mean precipitation forecasts with the increase of ensemble size. NEPS fails to resolve the heavier rainfall distribution and has the tendency to over-predict light precipitation. Ensembles with larger size predict the TC strike area of intense storms with higher probabilities due to better consensus among member tracks fueled by improved steering flow. But few cases have shown E11 or E22 perform equally well in forecasting TC propagation and in some cases even better compared to E44. Threshold oriented verification was carried out for winds at 850 hPa considering all the cases collectively. Brier Score (BS) and its components are very close to one another for higher thresholds of winds. In general, E44 has lower BS for all different thresholds with small lead time forecasts. Smaller ensembles have slightly better reliability while large ensembles improved the resolution for lower thresholds of wind speed. Continuous ranked probability score indicate that predicted probability distribution agrees well with the analyzed cumulative distribution. Area under relative operating characteristic curve exhibits better discriminating ability of probabilistic events with larger ensemble sizes. This study clearly showed that an increase in the ensemble size has significant beneficial impact on the reduction of the percentage of outliers thereby improving the potential predictive skill of the unexpected events.

Skill of medium-range reforecast for summertime extraordinary Arctic Cyclones in 1986–2016

The forecast skill for 26 extraordinary Arctic cyclones (EACs) in summer (June–August) during 1986–2016 was assessed using a reforecast data by the National Oceanic and Atmospheric Administration (NOAA)'s second-generation Global Ensemble Forecast System (GEFS). On average, more than 90% of ensemble members predicted the existence of the EACs for lead times of ≤3.0 days. The average central position error for the EACs decreases to ≤433.1 km (half the mean radius of the mature 26 EACs) for a lead time of 3.0 days, with the average central pressure error of ∼6.9 hPa. The GEFS reforecast for the 26 EACs shows a similar forecast skill to the GEFS reforecast and operational medium-range ensemble forecasts for 10 EACs occurred in summer during 2008–2016. There is no trend of improvements in forecast skills of existence, central pressure and position of the EACs at all lead times. In the verification of the strike probability for EACs within a 400-km radius, the probability information is useful in 1.0- to 5.0-day forecasts, although the forecast probabilities were overconfident for lead times more than 1.0 days.

Tropical Cyclone Forecasts Impact Assessment from the Assimilation of Hourly Visible, Shortwave, and Clear-Air Water Vapor Atmospheric Motion Vectors in HWRF

Abstract The assimilation of atmospheric motion vectors (AMVs) provides important wind information to conventional data-lacking oceanic regions, where tropical cyclones spend most of their lifetimes. Three new AMV types, shortwave infrared (SWIR), clear-air water vapor (CAWV), and visible (VIS), are produced hourly by NOAA/NESDIS and are assimilated in operational NWP systems. The new AMV data types are added to the hourly infrared (IR) and cloud-top water vapor (CTWV) AMV data in the 2016 operational version of the HWRF Model. In this study, we update existing quality control (QC) procedures and add new procedures specific to tropical cyclone assimilation. We assess the impact of the three new AMV types on tropical cyclone forecasts by conducting assimilation experiments for 25 Atlantic tropical cyclones from the 2015 and 2016 hurricane seasons. Forecasts are analyzed by considering all tropical cyclones as a group and classifying them into strong/weak storm vortices based on their initial model intensity. Metrics such as track error, intensity error, minimum central pressure error, and storm size are used to assess the data impact from the addition of the three new AMV types. Positive impact is obtained for these metrics, indicating that assimilating SWIR-, CAWV-, and VIS-type AMVs are beneficial for tropical cyclone forecasting. Given the results presented here, the new AMV types were accepted into NOAA/NCEP’s operational HWRF for the 2017 hurricane season.

Variational Analysis of Simulated Ocean Surface Winds from the Cyclone Global Navigation Satellite System (CYGNSS) and Evaluation Using a Regional OSSE

AbstractA positive impact of adding directional information to observations from the Cyclone Global Navigation Satellite System (CYNGSS) constellation of microsatellites is observed in simulation using a high-resolution nature run of an Atlantic hurricane for a 4-day period. Directional information is added using a two-dimensional variational analysis method (VAM) for near-surface vector winds that blends simulated CYGNSS wind speeds with an a priori background vector wind field at 6-h analysis times. The resulting wind vectors at CYGNSS data locations are more geophysically self-consistent when using high-resolution 6-h forecast backgrounds from a Hurricane Weather Research and Forecast Model (HWRF) control observing system simulation experiment (OSSE) compared to low-resolution 6-h forecasts from an associated Global Forecast System (GFS) model control OSSE. An important contributing factor is the large displacement error in the center of circulation in the GFS background wind fields that produces asymmetric circulations in the associated VAM analyses. Results of a limited OSSE indicate that CYGNSS winds reduce forecast error in hurricane intensity in 0–48-h forecasts compared to using no CYGNSS data. Assimilation of VAM-CYGNSS vector winds reduces maximum wind speed error by 2–5 kt (1 kt = 0.51 m s−1) and reduces minimum central pressure error by 2–5 hPa. The improvement in forecast intensity is notably larger and more consistent than the reduction in track error. The assimilation of VAM-CYGNSS wind vectors constrains analyses of surface wind field structures during OSSE more effectively than wind speeds alone. Because of incomplete sampling and the limitations of the data assimilation system used, CYGNSS scalar winds produce unwanted wind/pressure imbalances and asymmetries more often than the assimilation of VAM-CYGNSS data.

Medium‐Range Forecast Skill for Extraordinary Arctic Cyclones in Summer of 2008–2016

AbstractArctic cyclones (ACs) are a severe atmospheric phenomenon that affects the Arctic environment. This study assesses the forecast skill of five leading operational medium‐range ensemble forecasts for 10 extraordinary ACs that occurred in summer during 2008–2016. Average existence probability of the predicted ACs was >0.9 at lead times of ≤3.5 days. Average central position error of the predicted ACs was less than half of the mean radius of the 10 ACs (469.1 km) at lead times of 2.5–4.5 days. Average central pressure error of the predicted ACs was 5.5–10.7 hPa at such lead times. Therefore, the operational ensemble prediction systems generally predict the position of ACs within 469.1 km 2.5–4.5 days before they mature. The forecast skill for the extraordinary ACs is lower than that for midlatitude cyclones in the Northern Hemisphere but similar to that in the Southern Hemisphere.

비축대칭 3차원 모조 소용돌이를 이용한 열대저기압의 진로 및 강도예측

The bogussing method was further developed by incorporating the asymmetric component into the symmetric bogus tropical cyclone of the Structure Adjustable Balanced Vortex (SABV). The asymmetric component is separated from the disturbance field associated with the tropical cyclone by establishing local polar coordinates whose center is the location of the tropical cyclone. The relative importance of wave components in azimuthal direction was evaluated, and only two or three wave components with large amplitude are added to the symmetric components. Using the Weather Research and Forecast model (WRF), initialized with the asymmetric bogus vortex, the track and central pressure of tropical cyclones were predicted. Nine tropical cyclones, which passed over Korean peninsula during 2010~2012 were selected to assess the effect of asymmetric components. Compared to the symmetric bogus tropical cyclone, the track forecast error was reduced by about 18.9% and 17.4% for 48 hours and 72 hours forecast, while the central pressure error was not improved significantly. The results suggest that the inclusion of asymmetric component is necessary to improve the track forecast of tropical cyclones.

Predictability Characteristics of Landfalling Cyclones along the North American West Coast

Abstract The predictability of North Pacific cyclones can vary widely, from highly accurate prediction of storm intensity and location to forecast position errors of hundreds of kilometers and central pressure errors of tens of hectopascals. In this study, a Weather Research and Forecasting Model (WRF) ensemble Kalman filter is used to investigate predictability of landfalling cyclones on the west coast of North America over two winter seasons (2008/09 and 2009/10). Predictability is defined as the ensemble spread of cyclone central pressure at the final forecast time (24 h) where large spread means low predictability. Both ensemble spread and ensemble initial-condition sensitivity are examined for a wide variety of cyclones that occurred during the two seasons. Storms that are deepening and track from the southwest exhibit the largest ensemble initial-condition sensitivity and highest ensemble spread compared to decaying storms and storms that track from other directions. Storms that end south of 40°N, typically slow moving storms from the northwest, exhibit higher predictability regardless of whether or not they are deepening or decaying. Cyclones with large ensemble spread and low sensitivity are mature cyclones whose low predictability likely results from large initial-condition spread instead of large perturbation growth. These results highlight particular synoptic situations and cyclone characteristics that are associated with low predictability and can potentially be used to improve forecasts through improved observational coverage.

Evaluation of Multiple Dynamic Initialization Schemes for Tropical Cyclone Prediction

Abstract Three different dynamic initialization schemes for tropical cyclone (TC) prediction in numerical prediction systems are described and evaluated. The first scheme involves the removal of the analyzed vortex, followed by the insertion of a dynamically initialized vortex into the model analyses. This scheme is referred to as the tropical cyclone dynamic initialization scheme (TCDI) because the TC component is nudged to the observed surface pressure in an independent three-dimensional primitive equation model prior to insertion. The second scheme is a 12-h relaxation to the analyses' horizontal momentum before the forecast integration begins, and is called the dynamic initialization (DI) scheme. The third scheme is a combination of the previous two schemes, and is called the two-stage dynamic initialization scheme (TCDI/DI). In the first stage, TCDI is implemented in order to improve the representation of the TC vortex. In the second stage, DI is invoked in order to improve the balance between the inserted TC vortex and its environment. All three dynamic initialization schemes are compared with a control (CNTL) scheme, which creates the initial vortex using synthetic TC observations that match the observed intensity and structure in a three-dimensional variational data assimilation (3DVAR) system. The four schemes are tested on 120 cases in the North Atlantic and western North Pacific basins during 2010 and 2011 using the Naval Research Laboratory's TC prediction model: Coupled Ocean–Atmosphere Mesoscale Prediction System-Tropical Cyclones (COAMPS-TC). It is demonstrated that TCDI/DI performed the best overall with regard to intensity forecasts, reducing the average minimum central pressure error for all lead times by 24.4% compared to the CNTL scheme.

Spatial Distribution and Evolution of Extratropical Cyclone Errors over North America and its Adjacent Oceans in the NCEP Global Forecast System Model

Abstract Short- to medium-range (1–5 day) forecasts of extratropical cyclones around North America and its adjacent oceans are verified within the Global Forecast System (GFS) model at the National Centers for Environmental Prediction (NCEP) during the 2002–07 cool seasons (October–March). Cyclones in the immediate lee of the Rockies and U.S. Great Plains have 25%–50% smaller pressure errors than other regions after hour 36. The central pressure and displacement errors are largest over the central and eastern Pacific for the 42–72-h forecast, while the western and central Atlantic pressure errors for 96–120 h are similar to the central and eastern Pacific. For relatively strong cyclones, the western Atlantic and central/eastern Canada pressure errors are larger than those for the Pacific by 108–120 h. There are large spatial variations in the central pressure biases at 72–120 h, with overdeepened GFS cyclones (negative errors) extending from the northern Pacific and Bering Strait eastward to western Canada, while underdeepened GFS cyclones (positive errors) occur across northeast Canada and just east of the U.S. east coast. GFS cyclone tracks and spatial composites using the daily NCEP reanalysis are used to illustrate flow patterns and source regions for some of the large GFS cyclone errors and biases. Relatively large central pressure errors over the central Pacific early in the forecast (30 h) spread eastward over Canada by 66 h and the eastern United States by 84 h. The underdeepened GFS cyclone errors (>1.5 standard deviations) at day 4 over the western Atlantic are associated with an anomalous ridge over the western United States and trough over the eastern United States, and most of the underdeepening occurs with cyclones tracking east-northeastward across the Gulf Stream. Many of the overdeepened cyclones have tracks more parallel to the U.S. east coast. The underdeepened cyclones over the central and eastern Pacific tend to occur farther south (35°–45°N) than the overdeepened events.

Mesoscale air-sea interaction during the passage of a typhoon over the Yellow and East China Seas

In order to best capture the mutual forcing and feedback process to improve the accuracy of modeling of the mesoscale process, we developed the two-way coupling model MCM (Mesoscale Coupled Model) based on two independent models, the atmospheric model MM5v3 and the ocean model ECOM-si, using a flux coupler designed with the concurrent process communication technique. We selected Typhoon Winnie (9711) passing over the Yellow and East China Seas (YECS) to test the performance of the coupling model, and analyzed the quantitative impacts on atmospheric and oceanic variations. The results show that the simulated air-sea heat and momentum exchanges strongly affect circulation in both the atmosphere and the ocean. When mesoscale air-sea interaction is considered, the results for the atmosphere and ocean improved considerably. In terms of atmosphere, the air-sea interaction updates the distribution of elements (e.g., wind, heat flux, relative humidity and temperature) in the vertical and horizontal fields. The forecast result of typhoon intensity becomes more realistic, with decreases in central pressure error of 3.4 hPa at 30 hr and 9 hPa at 60 hr. A strong wind drives the cyclonic ocean current in the upper layer and induces horizontal transport and vertical mixing. All of these processes directly influence the Sea Surface Temperature (SST), which decreases over a large area, particularly near the typhoon center, and can reach 5°C. Finally, we discuss a possible negative feedback mechanism.

Weather Regimes and Forecast Errors in the Pacific Northwest

Abstract Despite overall improvements in numerical weather prediction and data assimilation, large short-term forecast errors of sea level pressure and 2-m temperature still occur. This is especially true for the west coast of North America where short-term numerical weather forecasts of surface low pressure systems can have large position and central pressure errors. In this study, forecast errors of sea level pressure and temperature in the Pacific Northwest are related to the shape of the large-scale flow aloft. Applying a hierarchical limited-contour clustering algorithm to historical 500-hPa geopotential height data produces four distinct weather regimes. The Rockies ridge regime, which exhibits a ridge near the axis of the Rocky Mountains and nearly zonal flow across the Pacific, experiences the highest magnitude and frequency of large sea level pressure errors. On the other hand, the coastal ridge regime, which exhibits a ridge aligned with the North American west coast, experiences the highest magnitude and frequency of large 2-m minimum temperature errors.

Major Numerical Forecast Failures over the Northeast Pacific

Abstract Strong North Pacific storms that impact the North American west coast are sometimes poorly predicted in the short term (up to 48 h) by operational models, with cyclone position errors of hundreds of kilometers and central pressure errors of tens of millibars. These major numerical forecast failures still occur despite continuing improvements in modeling and data assimilation. In this paper, the frequency and intensity of sea level pressure errors at buoy and coastal locations are documented by comparing the National Centers for Environmental Prediction (NCEP) Eta Model forecasts to observations and through case studies of two poorly forecast cyclones from the 2001/02 winter season. Using data from October 1999 through March 2003 at coastal and offshore sites along the west coast of North America, it was found that large forecast errors (48-h sea level pressure errors greater than 10 mb) by the Eta Model occur 10–15 times each winter, and extremely large errors (48-h errors greater than 15 mb) occ...

An Analysis of NMC's Nested Grid Model Forecasts of Alberta Clippers

Abstract Forecasts of Alberta Clipper–type cyclones defined as cyclones that move southeastward from regions of western Canada into south-central Canada or the north-central United States before moving eastward to the coast of North America, were studied using the National Meteorological Center's Nested Grid Model (NGM). All available NGM forecasts between 1 January 1988 and 31 December 1992, excluding the months of June, July, and August, were analyzed. The 5-yr sample included 59 Alberta Clippers. For each cast, the cyclone center position and central pressure were recorded for the NGM's initialization (0 h) and for 12-, 24-, 36-, and 48-h forecasts for each available run. Average cyclone position errors ranged from 140 km, with a standard deviation of 183 km at 12 h, to 339 km, with a standard deviation of 407 km at 48 h. Average central pressure errors ranged from 0.0 hPa, with a standard deviation of 2.5 hPa at 12 h, to 1.0 hPa, with a standard deviation of 5.7 hPa at 48 h. East of 84.0°W, cyclones v...

Evaluation of the Surface Prognoses of Cyclones and Anticyclones of the JMA and FNOC Models over East Asia and the Western Pacific during the 1983 Mei-Yu Season

Abstract This study compares the systematic errors of 36-h surface cyclone and anticyclone forecasts for two operational numerical weather prediction models over East Asia and the western North Pacific Ocean: the U.S. Navy's Operational Global Atmospheric Prediction System (NOGAPS), and Japan Meteorological Agency's Fine-mesh Limited Area Model (JFLM). The study is carried out for the 1983 Mei-Yu season (May–July), which is the wettest season over East Asia based on nontyphoon-produced rainfall. All available 0000 and 1200 GMT forecast runs are evaluated against an independent dataset of subjective analysis produced operationally by the Central Weather Bureau, Taipei. The mean position errors, mean central pressure errors and forecast skill indices for both cyclones and anticyclones in the NOGAPS and JFLM models are examined. Both NOGAPS and JFLM models are more likely to underforecast than to overforecast the existence and/or genesis of both cyclones and anticyclones. However, over the Tibetan Plateau an...