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Akira Yamazaki, Takemasa Miyoshi, Jun Inoue, Takeshi Enomoto, and Nobumasa Komori

Abstract

An ensemble-based forecast sensitivity to observations (EFSO) diagnosis has been implemented in an atmospheric general circulation model–ensemble Kalman filter data assimilation system to estimate the impacts of specific observations from the quasi-operational global observing system on weekly short-range forecasts. It was examined whether EFSO reasonably approximates the impacts of a subset of observations from specific geographical locations for 6-h forecasts, and how long the 6-h observation impacts can be retained during the 7-day forecast period. The reference for these forecasts was obtained from 12 data-denial experiments in each of which a subset of three radiosonde observations launched from a geographical location was excluded. The 12 locations were selected from three latitudinal bands comprising (i) four Arctic regions, (ii) four midlatitude regions in the Northern Hemisphere, and (iii) four tropical regions during the Northern Hemisphere winter of 2015/16. The estimated winter-averaged EFSO-derived observation impacts well corresponded to the 6-h observation impacts obtained by the data denials and EFSO could reasonably estimate the observation impacts by the data denials on short-range (from 6 h to 2 day) forecasts. Furthermore, during the medium-range (4–7 day) forecasts, it was found that the Arctic observations tend to seed the broadest impacts and their short-range observation impacts could be projected to beneficial impacts in Arctic and midlatitude North American areas. The midlatitude area was located just downstream of dynamical propagation from the Arctic toward the midlatitudes. Results obtained by repeated Arctic data-denial experiments were found to be generally common to those from the non-repeated experiments.

Open access
Jonathan M. Garner, William C. Iwasko, Tyler D. Jewel, Richard L. Thompson, and Bryan T. Smith

Abstract

A dataset maintained by the Storm Prediction Center (SPC) of 6300 tornado events from 2009 to 2015, consisting of radar-identified convective modes and near-storm environmental information obtained from Rapid Update Cycle and Rapid Refresh model analysis grids, has been augmented with additional radar information related to the low-level mesocyclones associated with tornado longevity, pathlength, and width. All EF2–EF5 tornadoes [as measured on the enhanced Fujita (EF) scale], in addition to randomly selected EF0–EF1 tornadoes, were extracted from the SPC dataset, which yielded 1268 events for inclusion in the current study. Analysis of those data revealed similar values of the effective-layer significant tornado parameter for the longest-lived (60+ min) tornadic circulations, longest-tracked (≥68 km) tornadoes, and widest tornadoes (≥1.2 km). However, the widest tornadoes occurring west of −94° longitude were associated with larger mean-layer convective available potential energy, storm-top divergence, and low-level rotational velocity. Furthermore, wide tornadoes occurred when low-level winds were out of the southeast, resulting in large low-level hodograph curvature and near-surface horizontal vorticity that was more purely streamwise when compared with long-lived and long-tracked events. On the other hand, tornado pathlength and longevity were maximized with eastward-migrating synoptic-scale cyclones associated with strong southwesterly wind profiles through much of the troposphere, fast storm motions, large values of bulk wind difference and storm-relative helicity, and lower buoyancy.

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Samantha Ferrett, Thomas H. A. Frame, John Methven, Christopher E. Holloway, Stuart Webster, Thorwald H. M. Stein, and Carlo Cafaro

Abstract

Forecasting rainfall in the tropics is a major challenge for numerical weather prediction. Convection-permitting (CP) models are intended to enable forecasts of high-impact weather events. Development and operation of these models in the tropics has only just been realized. This study describes and evaluates a suite of recently developed Met Office Unified Model CP ensemble forecasts over three domains in Southeast Asia, covering Malaysia, Indonesia, and the Philippines. The fractions skill score is used to assess the spatial scale dependence of skill in forecasts of precipitation during October 2018–March 2019. CP forecasts are skillful for 3-h precipitation accumulations at spatial scales greater than 200 km in all domains during the first day of forecasts. Skill decreases with lead time but varies depending on time of day over Malaysia and Indonesia, due to the importance of the diurnal cycle in driving rainfall in those regions. Skill is largest during daytime when precipitation is over land and is constrained by orography. Comparison of CP ensembles using 2.2-, 4.5-, and 8.8-km grid spacing and an 8.8-km ensemble with parameterized convection reveals that varying resolution has much less effect on ensemble skill and spread than the representation of convection. The parameterized ensemble is less skillful than CP ensembles over Malaysia and Indonesia and more skillful over the Philippines; however, the parameterized ensemble has large drops in skill and spread related to deficiencies in its diurnal cycle representation. All ensembles are underspread indicating that future model development should focus on this issue.

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Hussen Seid Endris, Linda Hirons, Zewdu Tessema Segele, Masilin Gudoshava, Steve Woolnough, and Guleid A. Artan

Abstract

The skill of precipitation forecasts from global prediction systems has a strong regional and seasonal dependence. Quantifying the skill of models for different regions and time scales is important, not only to improve forecast skill, but to enhance the effective uptake of forecast information. The Subseasonal to Seasonal Prediction project (S2S) database contains near-real-time forecasts and reforecasts from 11 operational centers and provides a great opportunity to evaluate and compare the skill of operational S2S systems. This study evaluates the skill of these state-of-the-art global prediction systems in predicting monthly precipitation over the Greater Horn of Africa. This comprehensive evaluation was performed using deterministic and probabilistic forecast verification metrics. Results from the analysis showed that the prediction skill varies with months and region. Generally, the models show high prediction skill during the start of the rainfall season in March and lower prediction skill during the peak of the rainfall in April. ECCC, ECMWF, KMA, NCEP, and UKMO show better prediction skill over the region for most of the months compared with the rest of the models. Conversely, BoM, CMA, HMCR, and ISAC show poor prediction skill over the region. Overall, the ECMWF model performs best over the region among the 11 models analyzed. Importantly, this study serves as a baseline skill assessment with the findings helping to inform how a subset of models could be selected to construct an objectively consolidated multimodel ensemble of S2S forecast products for the Greater Horn of Africa region, as recommended by the World Meteorological Organization.

Open access
Amit Bhardwaj, Vasubandhu Misra, Ben Kirtman, Tirusew Asefa, Carolina Maran, Kevin Morris, Ed Carter, Christopher Martinez, and Daniel Roberts

Abstract

We present here the analysis of 20 years of high-resolution experimental winter seasonal climate reforecasts for Florida (CLIFF). These winter seasonal reforecasts were dynamically downscaled by a regional atmospheric model at 10-km grid spacing from a global model run at T62 spectral resolution (~210-km grid spacing at the equator) forced with sea surface temperatures (SST) obtained from one of the global models in the North American Multimodel Ensemble (NMME). CLIFF was designed in consultation with water managers (in utilities and public water supply) in Florida targeting its five water management districts, including two smaller watersheds of two specific stakeholders in central Florida that manage the public water supply. This enterprise was undertaken in an attempt to meet the climate forecast needs of water management in Florida. CLIFF has 30 ensemble members per season generated by changes to the physics and the lateral boundary conditions of the regional atmospheric model. Both deterministic and probabilistic skill measures of the seasonal precipitation at the zero-month lead for November–December–January (NDJ) and one-month lead for December–January–February (DJF) show that CLIFF has higher seasonal prediction skill than persistence. The results of the seasonal prediction skill of land surface temperature are more sobering than precipitation, although, in many instances, it is still better than the persistence skill.

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Jihong Moon, Jinyoung Park, Dong-Hyun Cha, and Yumin Moon

Abstract

In this study, the characteristics of simulated tropical cyclones (TCs) over the western North Pacific by a regional model (the WRF Model) are verified. We utilize 12-km horizontal grid spacing, and simulations are integrated for 5 days from model initialization. A total of 125 forecasts are divided into five clusters through the k-means clustering method. The TCs in the cluster 1 and 2 (group 1), which includes many TCs moving northward in the subtropical region, generally have larger track errors than for TCs in cluster 3 and 4 (group 2). The optimal steering vector is used to examine the difference in the track forecast skill between these two groups. The bias in the steering vector between the model and analysis data is found to be more substantial for group 1 TCs than group 2 TCs. The larger steering vector difference for group 1 TCs indicates that environmental fields tend to be poorly simulated in group 1 TC cases. Furthermore, the residual terms, including the storm-scale process, asymmetric convection distribution, or beta-related effect, are also larger for group 1 TCs than group 2 TCs. Therefore, it is probable that the large track forecast error for group 1 TCs is a result of unreasonable simulations of environmental wind fields and residual processes in the midlatitudes.

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Huijun Huang, Jinnan Yuan, Guanhuan Wen, Xueyan Bi, Ling Huang, and Mingsen Zhou

Abstract

Tropical depressions formed over the South China Sea usually produce severe flooding and wind damage when they develop into a storm and make landfall. To provide an early warning, forecasters should know when, and if, a tropical depression will develop into a tropical storm. To better understand and predict such development, we examine the dynamic and thermodynamic variables of 74 tropical depressions over the South China Sea, 52 of which developed into storms, hereafter “developing,” with the remaining being classified as “nondeveloping.” Using the National Centers for Environmental Prediction Final (NCEP FNL) data, verified with ECMWF forecast data, we examine the dynamic and thermodynamic statistics that characterize these tropical cyclones. Based on these characteristics, we propose seven criteria to determine whether a tropical depression will develop. Five had been used before, but two new criteria are also found to be useful. These two are associated with the diabatic heating rate and help to determine whether a tropical cyclone diurnal cycle exists and whether the convection system remains intact in the center: 1) presence of a regular diurnal variation of the diabatic heating rate at the center and 2) occurrence of specific peaks in the radiative-heating profile. We test all seven criteria on all tropical depression cases in 2018/19 before the system developed or decayed, showing that these criteria can help to operationally identify whether or not a tropical depression develops into a tropical storm with an average lead time of 36.6 h.

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Eva-Maria Walz, Marlon Maranan, Roderick van der Linden, Andreas H. Fink, and Peter Knippertz

Abstract

Current numerical weather prediction models show limited skill in predicting low-latitude precipitation. To aid future improvements, be it with better dynamical or statistical models, we propose a well-defined benchmark forecast. We use the arguably best available high-resolution, gauge-calibrated, gridded precipitation product, the Integrated Multisatellite Retrievals for GPM (IMERG) “final run” in a ±15-day window around the date of interest to build an empirical climatological ensemble forecast. This window size is an optimal compromise between statistical robustness and flexibility to represent seasonal changes. We refer to this benchmark as extended probabilistic climatology (EPC) and compute it on a 0.1° × 0.1° grid for 40°S–40°N and the period 2001–19. To reduce and standardize information, a mixed Bernoulli–Gamma distribution is fitted to the empirical EPC, which hardly affects predictive performance. The EPC is then compared to 1-day ensemble predictions from the European Centre for Medium-Range Weather Forecasts (ECMWF) using standard verification scores. With respect to rainfall amount, ECMWF performs only slightly better than EPS over most of the low latitudes and worse over high-mountain and dry oceanic areas as well as over tropical Africa, where the lack of skill is also evident in independent station data. For rainfall occurrence, EPC is superior over most oceanic, coastal, and mountain regions, although the better potential predictive ability of ECMWF indicates that this is mostly due to calibration problems. To encourage the use of the new benchmark, we provide the data, scripts, and an interactive web tool to the scientific community.

Open access
Aaron J. Hill, Christopher C. Weiss, and David C. Dowell

Abstract

Ensemble forecasts are generated with and without the assimilation of near-surface observations from a portable, mesoscale network of StickNet platforms during the Verification of the Origins of Rotation in Tornadoes Experiment–Southeast (VORTEX-SE). Four VORTEX-SE intensive observing periods are selected to evaluate the impact of StickNet observations on forecasts and predictability of deep convection within the Southeast United States. StickNet observations are assimilated with an experimental version of the High-Resolution Rapid Refresh Ensemble (HRRRE) in one experiment, and withheld in a control forecast experiment. Overall, StickNet observations are found to effectively reduce mesoscale analysis and forecast errors of temperature and dewpoint. Differences in ensemble analyses between the two parallel experiments are maximized near the StickNet array and then either propagate away with the mean low-level flow through the forecast period or remain quasi-stationary, reducing local analysis biases. Forecast errors of temperature and dewpoint exhibit periods of improvement and degradation relative to the control forecast, and error increases are largely driven on the storm scale. Convection predictability, measured through subjective evaluation and objective verification of forecast updraft helicity, is driven more by when forecasts are initialized (i.e., more data assimilation cycles with conventional observations) rather than the inclusion of StickNet observations in data assimilation. It is hypothesized that the full impact of assimilating these data is not realized in part due to poor sampling of forecast sensitive regions by the StickNet platforms, as identified through ensemble sensitivity analysis.

Open access
Robert Conrick, Clifford F. Mass, Joseph P. Boomgard-Zagrodnik, and David Ovens

Abstract

During late summer 2020, large wildfires over the Pacific Northwest produced dense smoke that impacted the region for an extended period. During this period of poor air quality, persistent low-level cloud coverage was poorly forecast by operational numerical weather prediction models, which dissipated clouds too quickly or produced insufficient cloud coverage extent. This deficiency raises questions about the influence of wildfire smoke on low-level clouds in the marine environment of the Pacific Northwest. This paper investigates the effects of wildfire smoke on the properties of low-level clouds, including their formation, microphysical properties, and dissipation. A case study from 12 to 14 September 2020 is used as a testbed to evaluate the impact of wildfire smoke on such clouds. Observations from satellites and surface observing sites, coupled with mesoscale model simulations, are applied to understand the influence of wildfire smoke during this event. Results indicate that the presence of thick smoke over Washington led to decreased temperatures in the lower troposphere, which enhanced low-level cloud coverage, with smoke particles altering the microphysical structure of clouds to favor high concentrations of small droplets. Thermodynamic changes due to smoke are found to be the primary driver of enhanced cloud lifetime during these events, with microphysical changes to clouds as a secondary contributing factor. However, both the thermodynamic and microphysical effects are necessary to produce a realistic simulation.

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