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Xi Liu
,
Yu Zheng
,
Xiaoran Zhuang
,
Yaqiang Wang
,
Xin Li
,
Zhang Bei
, and
Wenhua Zhang

Abstract

The accurate prediction of short-term rainfall, and in particular the forecast of hourly heavy rainfall (HHR) probability, remains challenging for numerical weather prediction (NWP) models. Here, we introduce a deep learning (DL) model, PredRNNv2-AWS, a convolutional recurrent neural network designed for deterministic short-term rainfall forecasting. This model integrates surface rainfall observations and atmospheric variables simulated by the Precision Weather Analysis and Forecasting System (PWAFS). Our DL model produces realistic hourly rainfall forecasts for the next 13 h. Quantitative evaluations show that the use of surface rainfall observations as one of the predictors achieves higher performance (threat score) with 263% and 186% relative improvements over NWP simulations for the first 3 h and the entire forecast hours, respectively, at a threshold of 5 mm h−1. Noting that the optical-flow method also performs well in the initial hours, its predictions quickly worsen in the final hours compared to other experiments. The machine learning model, LightGBM, is then integrated to classify HHR from the predicted hourly rainfall of PredRNNv2-AWS. The results show that PredRNNv2-AWS can better reflect actual HHR conditions compared with PredRNNv2 and PWAFS. A representative case demonstrates the superiority of PredRNNv2-AWS in predicting the evolution of the rainy system, which substantially improves the accuracy of the HHR prediction. A test case involving the extreme flood event in Zhengzhou exemplifies the generalizability of our proposed model. Our model offers a reliable framework to predict target variables that can be obtained from numerical simulations and observations, e.g., visibility, wind power, solar energy, and air pollution.

Open access
Shu-Chih Yang
,
Yi-Pin Chang
,
Hsiang-Wen Cheng
,
Kuan-Jen Lin
,
Ya-Ting Tsai
,
Jing-Shan Hong
, and
Yu-Chi Li

Abstract

In this study, we investigate the impact of assimilating densely distributed Global Navigation Satellite System (GNSS) zenith total delay (ZTD) and surface station (SFC) data on the prediction of very short-term heavy rainfall associated with afternoon thunderstorm (AT) events in the Taipei basin. Under weak synoptic-scale conditions, four cases characterized by different rainfall features are chosen for investigation. Experiments are conducted with a 3-h assimilation period, followed by 3-h forecasts. Also, various experiments are performed to explore the sensitivity of AT initialization. Data assimilation experiments are conducted with a convective-scale Weather Research and Forecasting–local ensemble transform Kalman filter (WRF-LETKF) system. The results show that ZTD assimilation can provide effective moisture corrections. Assimilating SFC wind and temperature data could additionally improve the near-surface convergence and cold bias, further increasing the impact of ZTD assimilation. Frequently assimilating SFC data every 10 min provides the best forecast performance especially for rainfall intensity predictions. Such a benefit could still be identified in the earlier forecast initialized 2 h before the start of the event. Detailed analysis of a case on 22 July 2019 reveals that frequent assimilation provides initial conditions that can lead to fast vertical expansion of the convection and trigger an intense AT. This study proposes a new metric using the fraction skill score to construct an informative diagram to evaluate the location and intensity of heavy rainfall forecast and display a clear characteristic of different cases. Issues of how assimilation strategies affect the impact of ground-based observations in a convective ensemble data assimilation system and AT development are also discussed.

Significance Statement

In this study, we investigate the impact of frequently assimilating densely distributed ground-based observations on predicting four afternoon thunderstorm events in the Taipei basin. While assimilating GNSS-ZTD data can improve the moisture fields for initializing convection, assimilating surface station data improves the prediction of rainfall location and intensity, particularly when surface data are assimilated at a very high frequency of 10 min.

Open access
Peter J. Marinescu
,
Daniel Abdi
,
Kyle Hilburn
,
Isidora Jankov
, and
Liao-Fan Lin

Abstract

Estimates of soil moisture from two National Oceanic and Atmospheric Administration (NOAA) models are compared to in situ observations. The estimates are from a high-resolution atmospheric model with a land surface model [High-Resolution Rapid Refresh (HRRR) model] and a hydrologic model from the NOAA Climate Prediction Center (CPC). Both models produce wetter soils in dry regions and drier soils in wet regions, as compared to the in situ observations. These soil moisture differences occur at most soil depths but are larger at the deeper depths below the surface (100 cm). Comparisons of soil moisture variability are also assessed as a function of soil moisture regime. Both models have lower standard deviations as compared to the in situ observations for all soil moisture regimes. The HRRR model’s soil moisture is better correlated with in situ observations for drier soils as compared to wetter soils—a trend that was not present in the CPC model comparisons. In terms of seasonality, soil moisture comparisons vary depending on the metric, time of year, and soil moisture regime. Therefore, consideration of both the seasonality and soil moisture regime is needed to accurately determine model biases. These NOAA soil moisture estimates are used for a variety of forecasting and societal applications, and understanding their differences provides important context for their applications and can lead to model improvements.

Significance Statement

Soil moisture is an essential variable coupling the land surface to the atmosphere. Accurate estimates of soil moisture are important for forecasting near-surface temperature and moisture, predicting where clouds will form, and assessing drought and fire risks. There are multiple estimates of soil moisture available, and in this study, we compare soil moisture estimates from two different National Oceanic and Atmospheric Administration (NOAA) models to in situ observations. These comparisons include both soil moisture amount and variability and are conducted at several soil depths, in different soil moisture regimes, and for different seasons and years. This comprehensive assessment allows for an accurate assessment of biases within these models that would be missed when conducting analyses more broadly.

Open access
Stephanie S. Rushley
,
Matthew A. Janiga
,
William Crawford
,
Carolyn A. Reynolds
,
William Komaromi
, and
Justin McLay

Abstract

Accurately simulating the Madden–Julian oscillation (MJO), which dominates intraseasonal (30–90 day) variability in the tropics, is critical to predicting tropical cyclones (TCs) and other phenomena at extended-range (2–3 week) time scales. MJO biases in intensity and propagation speed are a common problem in global coupled models. For example, the MJO in the Navy Earth System Prediction Capability (ESPC), a global coupled model, has been shown to be too strong and too fast, which has implications for the MJO–TC relationship in that model. The biases and extended-range prediction skill in the operational version of the Navy ESPC are compared to experiments applying different versions of analysis correction-based additive inflation (ACAI) to reduce model biases. ACAI is a method in which time-mean and stochastic perturbations based on analysis increments are added to the model tendencies with the goals of reducing systematic error and accounting for model uncertainty. Over the extended boreal summer (May–November), ACAI reduces the root-mean-squared error (RMSE) and improves the spread–skill relationship of the total tropical and MJO-filtered OLR and low-level zonal winds. While ACAI improves skill in the environmental fields of low-level absolute vorticity, potential intensity, and vertical wind shear, it degrades the skill in the relative humidity, which increases the positive bias in the genesis potential index (GPI) in the operational Navy ESPC. Northern Hemisphere integrated TC genesis biases are reduced (increased number of TCs) in the ACAI experiments, which is consistent with the positive GPI bias in the ACAI simulations.

Open access
David S. Richardson
,
Hannah L. Cloke
,
John A. Methven
, and
Florian Pappenberger

Abstract

We investigate the run-to-run consistency (jumpiness) of ensemble forecasts of tropical cyclone tracks from three global centers: ECMWF, the Met Office, and NCEP. We use a divergence function to quantify the change in cross-track position between consecutive ensemble forecasts initialized at 12-h intervals. Results for the 2019–21 North Atlantic hurricane season show that the jumpiness varied substantially between cases and centers, with no common cause across the different ensemble systems. Recent upgrades to the Met Office and NCEP ensembles reduced their overall jumpiness to match that of the ECMWF ensemble. The average divergence over the set of cases provides an objective measure of the expected change in cross-track position from one forecast to the next. For example, a user should expect on average that the ensemble mean position will change by around 80–90 km in the cross-track direction between a forecast for 120 h ahead and the updated forecast made 12 h later for the same valid time. This quantitative information can support users’ decision-making, for example, in deciding whether to act now or wait for the next forecast. We did not find any link between jumpiness and skill, indicating that users should not rely on the consistency between successive forecasts as a measure of confidence. Instead, we suggest that users should use ensemble spread and probabilistic information to assess forecast uncertainty, and consider multimodel combinations to reduce the effects of jumpiness.

Significance Statement

Forecasting the tracks of tropical cyclones is essential to mitigate their impacts on society. Numerical weather prediction models provide valuable guidance, but occasionally there is a large jump in the predicted track from one run to the next. This jumpiness complicates the creation and communication of consistent forecast advisories and early warnings. In this work we aim to better understand forecast jumpiness and we provide practical information to forecasters to help them better use the model guidance. We show that the jumpiest cases are different for different modeling centers, that recent model upgrades have reduced forecast jumpiness, and that there is not a strong link between jumpiness and forecast skill.

Open access
Joseph A. Grim
,
James O. Pinto
, and
David C. Dowell

Abstract

This study provides a comparison of the operational HRRR version 4 and its eventual successor, the experimental Rapid Refresh Forecast System (RRFS) model (summer 2022 version), at predicting the evolution of convective storm characteristics during widespread convective events that occurred primarily over the eastern United States during summer 2022. In total 32 widespread convective events were selected using observations from the MRMS composite reflectivity, which includes an equal number of MCSs, quasi-linear convective systems (QLCSs), clusters, and cellular convection. Each storm system was assessed on four primary characteristics: total storm area, total storm count, storm area ratio (an indicator of mean storm size), and storm size distributions. It was found that the HRRR predictions of total storm area were comparable to MRMS, while the RRFS overpredicted total storm area by 40%–60% depending on forecast lead time. Both models tended to underpredict storm counts particularly during the storm initiation and growth period. This bias in storm counts originates early in the model runs (forecast hour 1) and propagates through the simulation in both models indicating that both miss storm initiation events and/or merge individual storm objects too quickly. Thus, both models end up with mean storm sizes that are much larger than observed (RRFS more so than HRRR). Additional analyses revealed that the storm area and individual storm biases were largest for the clusters and cellular convective modes. These results can serve as a benchmark for assessing future versions of RRFS and will aid model users in interpreting forecast guidance.

Open access
Pamela L. Heinselman
,
Patrick C. Burke
,
Louis J. Wicker
,
Adam J. Clark
,
John S. Kain
,
Jidong Gao
,
Nusrat Yussouf
,
Thomas A. Jones
,
Patrick S. Skinner
,
Corey K. Potvin
,
Katie A. Wilson
,
Burkely T. Gallo
,
Montgomery L. Flora
,
Joshua Martin
,
Gerry Creager
,
Kent H. Knopfmeier
,
Yunheng Wang
,
Brian C. Matilla
,
David C. Dowell
,
Edward R. Mansell
,
Brett Roberts
,
Kimberly A. Hoogewind
,
Derek R. Stratman
,
Jorge Guerra
,
Anthony E. Reinhart
,
Christopher A. Kerr
, and
William Miller

Abstract

In 2009, advancements in NWP and computing power inspired a vision to advance hazardous weather warnings from a warn-on-detection to a warn-on-forecast paradigm. This vision would require not only the prediction of individual thunderstorms and their attributes but the likelihood of their occurrence in time and space. During the last decade, the warn-on-forecast research team at the NOAA National Severe Storms Laboratory met this challenge through the research and development of 1) an ensemble of high-resolution convection-allowing models; 2) ensemble- and variational-based assimilation of weather radar, satellite, and conventional observations; and 3) unique postprocessing and verification techniques, culminating in the experimental Warn-on-Forecast System (WoFS). Since 2017, we have directly engaged users in the testing, evaluation, and visualization of this system to ensure that WoFS guidance is usable and useful to operational forecasters at NOAA national centers and local offices responsible for forecasting severe weather, tornadoes, and flash floods across the watch-to-warning continuum. Although an experimental WoFS is now a reality, we close by discussing many of the exciting opportunities remaining, including folding this system into the Unified Forecast System, transitioning WoFS into NWS operations, and pursuing next-decade science goals for further advancing storm-scale prediction.

Significance Statement

The purpose of this research is to develop an experimental prediction system that forecasts the probability for severe weather hazards associated with individual thunderstorms up to 6 h in advance. This capability is important because some people and organizations, like those living in mobile homes, caring for patients in hospitals, or managing large outdoor events, require extended lead time to protect themselves and others from potential severe weather hazards. Our results demonstrate a prediction system that enables forecasters, for the first time, to message probabilistic hazard information associated with individual severe storms between the watch-to-warning time frame within the United States.

Open access
Ji-Hoon Ha
and
Hyesook Lee

Abstract

The optical flow technique has advantages in motion tracking and has long been employed in precipitation nowcasting to track the motion of precipitation fields using ground radar datasets. However, the performance and forecast time scale of models based on optical flow are limited. Here, we present the results of the application of the deep learning method to optical flow estimation to extend its forecast time scale and enhance the performance of nowcasting. It is shown that a deep learning model can better capture both multispatial and multitemporal motions of precipitation events compared with traditional optical flow estimation methods. The model comprises two components: 1) a regression process based on multiple optical flow algorithms, which more accurately captures multispatial features compared with a single optical flow algorithm; and 2) a U-Net-based network that trains multitemporal features of precipitation movement. We evaluated the model performance with cases of precipitation in South Korea. In particular, the regression process minimizes errors by combining multiple optical flow algorithms with a gradient descent method and outperforms other models using only a single optical flow algorithm up to a 3-h lead time. Additionally, the U-Net plays a crucial role in capturing nonlinear motion that cannot be captured by a simple advection model through traditional optical flow estimation. Consequently, we suggest that the proposed optical flow estimation method with deep learning could play a significant role in improving the performance of current operational nowcasting models, which are based on traditional optical flow methods.

Significance Statement

The purpose of this study is to improve the accuracy of short-term rainfall prediction based on optical flow methods that have been employed for operational precipitation nowcasting. By utilizing open-source libraries, such as OpenCV, and commonly applied machine learning techniques, such as multiple linear regression and U-Net networks, we propose an accessible model for enhancing prediction accuracy. We expect that the improvement in prediction accuracy will significantly improve the practical application of operational precipitation nowcasting.

Open access
Alex Alvin Cheung
,
Christopher J. Slocum
,
John A. Knaff
, and
Muhammad Naufal Razin

Abstract

Intense tropical cyclones can form secondary eyewalls (SEs) that contract toward the storm center and eventually replace the inner eyewall, a process known as an eyewall replacement cycle (ERC). However, SE formation does not guarantee an eventual ERC, and often, SEs follow differing evolutionary pathways. This study documents SE evolution and progressions observed in numerous tropical cyclones, and results in two new datasets using passive microwave imagery: a global subjectively labeled dataset of SEs and eyes and their uncertainties from 72 storms between 2016 and 2019, and a dataset of 87 SE progressions that highlights the broad convective organization preceding and following an SE formation. The results show that two primary SE pathways exist: “No Replacement,” known as “Path 1,” and “Replacement,” known as the “Classic Path.” Most interestingly, 53% of the most certain SE formations result in an eyewall replacement. The Classic Path is associated with stronger column average meridional wind, a faster poleward component of storm motion, more intense storms, weaker vertical wind shear, greater relative humidity, a larger storm wind field, and stronger cold-air advection. This study highlights that a greater number of potential SE pathways exist than previously thought. The results of this study detail several observational features of SE evolution that raise questions about the physical processes that drive SE formations. Most important, environmental conditions and storm metrics identified here provide guidance for predictors in artificial intelligence applications for future tropical cyclone SE detection algorithms.

Open access
Simon Ageet
,
Andreas H. Fink
,
Marlon Maranan
, and
Benedikt Schulz

Abstract

Despite the enormous potential of precipitation forecasts to save lives and property in Africa, low skill has limited their uptake. To assess the skill and improve the performance of the forecast, validation and postprocessing should continuously be carried out. Here, we evaluate the quality of reforecasts from the European Centre for Medium-Range Weather Forecasts over equatorial East Africa (EEA) against satellite and rain gauge observations for the period 2001–18. The 24-h rainfall accumulations are analyzed from short- to medium-range time scales. Additionally, 48- and 120-h rainfall accumulations were also assessed. The skill was assessed using an extended probabilistic climatology (EPC) derived from the observations. Results show that the reforecasts overestimate rainfall, especially during the rainy seasons and over high-altitude areas. However, there is potential of skill in the raw forecasts up to 14-day lead time. There is an improvement of up to 30% in the Brier score/continuous ranked probability score relative to EPC in most areas, especially the higher-altitude regions, decreasing with lead time. Aggregating the reforecasts enhances the skill further, likely due to a reduction in timing mismatches. However, for some regions of the study domain, the predictive performance is worse than EPC, mainly due to biases. Postprocessing the reforecasts using isotonic distributional regression considerably improves skill, increasing the number of grid points with positive Brier skill score (continuous ranked probability skill score) by an average of 81% (91%) for lead times 1–14 days ahead. Overall, the study highlights the potential of the reforecasts, the spatiotemporal variation in skill, and the benefit of postprocessing in EEA.

Open access