Browse

You are looking at 71 - 80 of 3,010 items for :

  • Weather and Forecasting x
  • Refine by Access: All Content x
Clear All
Maria Gehne
,
Brandon Wolding
,
Juliana Dias
, and
George N. Kiladis

Abstract

Tropical precipitation and circulation are often coupled and span a vast spectrum of scales from a few to several thousands of kilometers and from hours to weeks. Current operational numerical weather prediction (NWP) models struggle with representing the full range of scales of tropical phenomena. Synoptic to planetary scales are of particular importance because improved skill in the representation of tropical larger-scale features such as convectively coupled equatorial waves (CCEWs) has the potential to reduce forecast error propagation from the tropics to the midlatitudes. Here we introduce diagnostics from a recently developed tropical variability diagnostics toolbox, where we focus on two recent versions of NOAA’s Unified Forecast System (UFS): operational GFSv15 forecasts and experimental GFSv16 forecasts from April to October 2020. The diagnostics include space–time coherence spectra to identify preferred scales of coupling between circulation and precipitation, pattern correlations of Hovmöller diagrams to assess model skill in zonal propagation of precipitating features, CCEW skill assessment, plus a diagnostic aimed at evaluating moisture–convection coupling in the tropics. Results show that the GFSv16 forecasts are slightly more realistic than GFSv15 in their coherence between precipitation and model dynamics at synoptic to planetary scales, with modest improvements in moisture convection coupling. However, this slightly improved performance does not necessarily translate to improvements in traditional precipitation skill scores. The results highlight the utility of these diagnostics in the pursuit of better understanding of NWP model performance in the tropics, while also demonstrating the challenges in translating model advancements into improved skill.

Restricted access
Jason A. Sippel
,
Xingren Wu
,
Sarah D. Ditchek
,
Vijay Tallapragada
, and
Daryl T. Kleist

Abstract

This study reviews the recent addition of dropwindsonde wind data near the tropical cyclone (TC) center as well as the first-time addition of high-density, flight-level reconnaissance observations (HDOBs) into the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS). The main finding is that the additional data have profound positive impacts on subsequent TC track forecasts. For TCs in the North Atlantic (NATL) basin, statistically significant improvements in track extend through 4–5 days during reconnaissance periods. Further assessment suggests that greater improvements might also be expected at days 6–7. This study also explores the importance of comprehensively assessing data impact. For example, model or data assimilation changes can affect the so-called “early” and “late” versions of the forecast very differently. It is also important to explore different ways to describe the error statistics. In several instances the impacts of the additional data strongly differ depending on whether one examines the mean or median errors. The results demonstrate the tremendous potential for further improving TC forecasts. The data added here were already operationally transmitted and assimilated by other systems at NCEP, and many further improvements likely await with improved use of these and other reconnaissance observations. This demonstrates the need of not only investing in data assimilation improvements, but also enhancements to observational systems in order to reach next-generation hurricane forecasting goals.

Significance Statement

This study demonstrates that data gathered from reconnaissance missions into tropical cyclones substantially improves tropical cyclone track forecasts.

Restricted access
Jean-François Caron
and
Mark Buehner

Abstract

The approach of applying different amounts of horizontal localization to different ranges of background-error covariance horizontal scales as proposed by Buehner and Shlyaeva was recently implemented in the four-dimensional ensemble–variational (4DEnVar) data assimilation scheme of the global deterministic prediction system (GDPS) at Environment and Climate Change Canada operations. To maximize the benefits from this approach to reduce the sampling noise in the ensemble-derived background-error covariances, it was necessary to adopt a new weighting between the climatological and flow-dependent covariances that increases significantly the role of the latter. Thus, in December 2021 the GDPS became the first operational global deterministic medium-range weather forecasting system to rely completely on flow-dependent covariances in the troposphere and the lower stratosphere. The experiments that led to the adoption of these two related changes and their impacts on the forecasts up to 7 days for various regions of the globe during the boreal summer of 2019 and winter of 2020 are presented here. It is also illustrated that relying more on ensemble-derived covariances amplifies the positive impacts on the GDPS when the background ensemble generation strategy is improved.

Open access
Kuan-Jen Lin
,
Shu-Chih Yang
, and
Shuyi S. Chen

Abstract

This study investigates the impact of assimilating ground-based radar reflectivity and wind data on tropical cyclone (TC) intensity prediction. The effect on a high-impact TC in the western North Pacific region that penetrated the Bashi Channel is examined. A multiscale correction based on the successive covariance localization (SCL) method is adopted to improve the analysis and forecast performance. In addition, GNSS-R wind speed is assimilated jointly in the rapid update assimilation framework to complement the TC boundary layer where radar data are limited. Model experiments are conducted and evaluated using the observing system simulation experiments (OSSEs) framework with a coupled atmosphere–ocean model nature run. Taking the experiment without data assimilation as the baseline, assimilating the radar data with the standard localization and SCL methods reduces the wind speed analysis error by 12% and 44%, respectively. The SCL method dominates the improvement in TC intensity prediction with a lead time longer than 2 days and the TC’s peak intensity forecast is improved by 18 hPa. The additional assimilation of the GNSS-R wind speed observation further reduces the wind speed error in the low-level analysis by 12% and 5% under the standard and SCL radar assimilation framework, respectively. GNSS-R wind assimilation leads to a further 6-hPa improvement in TC’s peak intensity. However, the sampling error introduced by the SCL method restrains the effect of GNSS-R assimilation. Sensitivity experiments with different GNSS-R data arrangements show that better GNSS-R wind coverage and additional wind direction information can further improve the TC analysis.

Significance Statement

Tropical cyclone (TC) intensity prediction over the western North Pacific (WNP) region remains a significant challenge due to limited observations. This study aims to improve the TC intensity prediction in WNP by assimilating the ground-based radar data using a multiscale correction framework and incorporating with the satellite ocean surface wind speed observation. We particularly focus on a high-impact TC like Typhoon Hato (2017), which penetrated the Bashi Channel and later made landfall in China, causing great damage. Our results showed that the assimilation strategy improved the TC intensity prediction for a lead time longer than 2 days. These results demonstrate the great potential of these observations and can provide guidance for future applications in operation centers.

Open access
Jen-Ping Chen
,
Tzu-Chin Tsai
,
Min-Duan Tzeng
,
Chi-Shuin Liao
,
Hung-Chi Kuo
, and
Jing-Shan Hong

Abstract

Microphysical perturbation experiments were conducted to investigate the sensitivity of convective heavy rain simulation to cloud microphysical parameterization and its feasibility for ensemble forecasts. An ensemble of 20 perturbation members differing in either the microphysics package or process treatments within a single scheme was applied to simulate 10 summer-afternoon heavy-rain convection cases. The simulations revealed substantial disagreements in the location and amplitude of peak rainfall among the microphysics-package and single-scheme members, with an overall spread of 57%–161%, 66%–161%, and 65%–149% of the observed average rainfall, maximum rainfall, and maximum intensity, respectively. The single-scheme members revealed that the simulation of heavy convective precipitation is quite sensitive to factors including ice-particle fall speed parameterization, aerosol type, ice particle shape, and size distribution representation. The microphysical ensemble can derive reasonable probability of occurrence for a location-specific heavy-rain forecast. Spatial-forecast performance indices up to 0.6 were attained by applying an optimal fuzzy radius of about 8 km for the warning-area coverage. The forecasts tend to be more successful for more organized convection. Spectral mapping methods were further applied to provide ensemble forecasts for the 10 heavy rainfall cases. For most cases, realistic spatial patterns were derived with spatial correlation up to 0.8. The quantitative performance in average rainfall, maximum rainfall, and maximum intensity from the ensembles reached correlations of 0.83, 0.84, and 0.51, respectively, with the observed values.

Significance Statement

Heavy rainfall from summer convections is stochastic in terms of intensity and location; therefore, an accurate deterministic forecast is often challenging. We designed perturbation experiments to explore weather forecasting models’ sensitivity to cloud microphysical parameterizations and the feasibility of application to ensemble forecast. Promising results were obtained from simulations of 10 real cases. The cloud microphysical ensemble approach may provide reasonable forecasts of heavy rainfall probability and convincing rainfall spatial distribution, particularly for more organized convection.

Restricted access
Andrew C. Winters
and
Hannah E. Attard

Abstract

Cool season (September–May) extreme temperature and precipitation events are frequently tied to surface cyclones and anticyclones, which are modulated on the synoptic scale by the state and evolution of the upper-tropospheric jet stream. This study adopts a jet-centered approach to classify the prevailing large-scale flow pattern into jet regimes based on the leading modes of variability of the North Pacific jet (NPJ) and the North Atlantic jet (NAJ), respectively. The characteristics of these joint NPJ–NAJ regimes are subsequently examined using composite analysis to identify large-scale flow environments conducive to the occurrence of anomalous temperatures and precipitation across North America. The analysis reveals that composite large-scale flow environments associated with each joint NPJ–NAJ regime can be approximated as a linear combination of the separate large-scale environments that characterize each NPJ regime and NAJ regime independently. Furthermore, knowledge of the joint NPJ–NAJ regime provides more precision regarding the relative likelihood and spatial coverage of anomalous temperatures and precipitation than would be obtained from consideration of the NPJ or NAJ regime in isolation. The frequencies of each joint NPJ–NAJ regime can also be modulated by the occurrence of sudden stratospheric warmings, with increased frequencies of an equatorward-shifted NAJ and a retracted NPJ during the 30-day period following a warming event. The results from the present study demonstrate that knowledge of the joint NPJ–NAJ regime exhibits the potential to inform forecasts of anomalous temperatures and precipitation at medium-range and subseasonal time scales.

Significance Statement

The development of cool season temperature and precipitation extremes are modulated by the state and evolution of the upper-tropospheric jet stream. Therefore, this study adopts a jet-centered approach to quantify the extent to which temperature and precipitation extremes over North America are related to the coevolution of the North Pacific and North Atlantic segments of the jet stream. The analysis demonstrates that the relative likelihood and spatial coverage of temperature and precipitation extremes varies significantly across North America based on the combined state of the North Pacific and North Atlantic jets. The jet-centered approach utilized in this study exhibits the potential to inform operational medium-range (6–10 day) and subseasonal forecasts of temperature and precipitation across North America.

Restricted access
Ryohei Kato
,
Shingo Shimizu
,
Tadayasu Ohigashi
,
Takeshi Maesaka
,
Ken-ichi Shimose
, and
Koyuru Iwanami

Abstract

Meso-γ-scale (2–20 km) local heavy rain (LHR) can cause fatalities through the sudden rise of rivers and flooding of roads. To help prevent this loss of life, we developed prediction methods for these types of meteorological hazards. We assimilated ground-based cloud radar (Ka-band radar) data that can capture cloud droplets before raindrops form and attempted to predict LHR with a cloud resolving numerical weather prediction (NWP) model. High-temporal (1-min interval) three-dimensional cloud radar data obtained through special observation were assimilated using a water vapor nudging method in the pre-rain stage of an LHR-causing cumulonimbus. While rainfall was not predicted by the NWP model without assimilation, LHR was predicted approximately 20 min after the conclusion of cloud radar data assimilation cycling. Results suggest that NWP with cloud radar data assimilation in the pre-rain stage has great potential for predicting LHR, and can lead to an early evacuation warning and subsequent evacuation of vulnerable populations.

Significance Statement

The development of prediction methods for local (within several kilometers) heavy rain (LHR) is important because LHR events can cause deaths through the sudden rise of rivers and flooding of roads by rapidly developing (≤30 min) rain clouds. This study aims to develop a method for predicting LHR even before it begins to rain, which has been difficult to date. Using a technique called data assimilation, which integrates observation and simulation, we developed a method for assimilating cloud radar observations that can capture cloud droplets before raindrops form. As a result, we succeeded in predicting LHR before rainfall commenced. By extending and applying this research, early evacuation of vulnerable populations during LHR is possible.

Open access
Thomas L. Gard
,
Henry E. Fuelberg
, and
John L. Cintineo

Abstract

Pulse severe storms are single-cell thunderstorms that produce severe wind and/or severe hail for a brief period of time. These storms pose a major warm season forecasting problem since forecasters presently do not have sufficient guidance to know which, if any, of the cells that are observed will become severe. The empirical Probability of Severe (ProbSevere) model, developed by the Cooperative Institute for Meteorological Satellite Studies (CIMSS), fuses real-time data to produce short-term (0–60 min), statistically derived probabilistic forecasts of thunderstorm intensity. This study evaluates the ability of ProbSevere to predict pulse severe storms in the southeast United States. ProbSevere objects fitting the usual definition of a pulse severe environment were matched with severe events from Storm Data to create a dataset of ProbSevere objects that corresponded to pulse severe thunderstorms. A null dataset consisted of objects in pulse severe environments that did not match with a severe event. Results reveal that ProbSevere’s probabilities are small to moderate at the times corresponding to pulse severe events. While probabilities of nonsevere storms are generally smaller, there are a large number of outliers. Lightning flash rate is the only predictor relevant to this study that correlates strongly with increasingly favorable pulse storm probabilities. We conclude that ProbSevere provides forecasters only limited guidance as to whether a pulse severe event will soon occur. Developing a version of ProbSevere specifically for pulse severe storms would likely lead to better predictability for this mode of convection.

Restricted access
Peter R. Gent
Open access
Szymon Poręba
,
Mateusz Taszarek
, and
Zbigniew Ustrnul

Abstract

The relationship between convective parameters derived from ERA5 and cloud-to-ground (CG) lightning flashes from the PERUN network in Poland was evaluated. All flashes detected between 2002 and 2019 were divided into intensity categories based on a peak 1-min CG lightning flash rate and were collocated with proximal profiles from ERA5 to assess their climatological variability. Thunderstorms in Poland are the most frequent in July, between 1400 and 1600 UTC and over the southeastern parts of the country. The highest median of most unstable convective available potential energy (MUCAPE) for CG lightning flash events is from June to August, between 1400 and 1600 UTC (around 900 J kg−1), whereas patterns in 0–6-km wind shear [deep-layer shear (DLS)] are reversed, with the highest median values during winter and night (around 25 m s−1). The best overlap of MUCAPE and DLS (MUWMAXSHEAR parameter) is in July–August, typically between 1400 and 2000 UTC with median values of around 850 m2 s−2. Thunderstorms in Poland are the most frequent in MUCAPE below 1000 J kg−1, and DLS between 8 and 15 m s−1. Along with increasing MUCAPE and DLS, peak CG lightning flash rates increase as well. Compared to DLS, MUCAPE is a more important parameter in forecasting any lightning activity, but when these two are combined together (MUWMAXSHEAR) they are more reliable in distinguishing between thunderstorms producing small and high CG lightning flash rates. Our results also indicate that higher CG lightning flash rates result in thunderstorms more frequently associated with severe weather reports (hail, tornado, wind).

Significance Statement

Each year severe thunderstorms produce considerable material losses and lead to deaths across central Europe; thus, a better understanding of local storm climatologies and their accompanying environments is important for operational forecasters, emergency managers, and risk estimation. In this research we address this issue by analyzing 18 years of lightning intensity data and collocated atmospheric environments. Thunderstorms in Poland are the most frequent in July between 1400 and 1600 UTC and form typically in environments with low atmospheric instability and moderate vertical shear of the horizontal wind. The probability for storms producing intense lightning increases when both of these environmental parameters reach higher values.

Open access