Why does convection weaken over Sumatra Island in an active phase of the MJO?

This study investigated the diurnal cycle of convection over Sumatra Island in an active phase of the Madden-Julian Oscillation (MJO) during the Pre-Years of the Maritime Continent (YMC) observation campaign in December 2015 based on in-situ and satellite observations and a convection-permitting numerical model. Observations suggest that before the active phase of the MJO in early December, convection occurred frequently over the island during the afternoon and at midnight. By contrast, during the active phase of the MJO in mid-December, afternoon convection over the island was delayed and suppressed, and midnight convection was suppressed. Numerical experiments also successfully replicated the main features of the observed modulations. In general, during the active phase of the MJO, the troposphere became drier in the Sumatra region. While the clouds reduced the solar radiation over the land, the sea breeze was also found to be delayed and weakened. As a result, the afternoon convection initiation was delayed and weakened. Further analyses suggested that the sea breeze was weakened mainly due to the orographic stagnation effect rather than the slightly reduced land-sea temperature contrast. On the other hand, the increased stratiform-anvil clouds induced the anomalous evaporative cooling in the mid-troposphere and generated island-scale subsidence during the nighttime, which finally led to the suppression of inland convection. Overall, our study reveals the modulation of diurnal convection over Sumatra Island by an active phase of the MJO and also shows the potential role of land-sea interaction in convection initiation and maintenance.

Benefits of smoothing backgrounds and radar reflectivity observations for multiscale data assimilation with an ensemble Kalman filter at convective scales: A proof of concept study

In the ensemble Kalman filter (EnKF), the covariance localization radius is usually small when assimilating radar observations because of high density of the radar observations. This makes the region away from precipitation difficult to correct if no other observations are available, as there is no reason to correct the background. To correct errors away from the innovating radar observations, a multiscale localization (MLoc) method adapted to dense observations like those from radar is proposed. In this method, different scales are corrected successively by using the same reflectivity observations, but with different degree of smoothing and localization radius at each step. In the context of observing system simulation experiments, single and multiple assimilation experiments are conducted with the MLoc method. Results show that the MLoc assimilation updates areas that are away from the innovative observations and improves on average the analysis and forecast quality in single cycle and cycling assimilation experiments. The forecast gains are maintained until the end of the forecast period, illustrating the benefits of correcting different scales.

The Multigrid Beta Function Approach for Modeling of Background Error Covariance in the Real-Time Mesoscale Analysis (RTMA)

We describe a method for the efficient generation of the covariance operators of a variational data assimilation scheme which is suited to implementation on a massively parallel computer. The elementary components of this scheme are what we call ‘beta filters’, since they are based on the same spatial profiles possessed by the symmetric beta distributions of probability theory. These approximately Gaussian (bell-shaped) polynomials blend smoothly to zero at the ends of finite intervals, which makes them better suited to parallelization than the present quasi-Gaussian ‘recursive filters’ used in operations at NCEP. These basic elements are further combined at a hierarchy of different spatial scales into an overall multigrid structure formulated to preserve the necessary self-adjoint attribute possessed by any valid covariance operator. This paper describes the underlying idea of the beta filter and discusses how generalized Helmholtz operators can be enlisted to weight the elementary contributions additively in such a way that the covariance operators may exhibit realistic negative sidelobes, which are not easily obtained through the recursive filter paradigm. The main focus of the paper is on the basic logistics of the multigrid structure by which more general covariance forms are synthesized from the basic quasi-Gaussian elements. We describe several ideas on how best to organize computation, which led us to a generalization of this structure which made it practical so that it can efficiently perform with any rectangular arrangement of processing elements. Some simple idealized examples of the applications of these ideas are given.

Using a cost-effective approach to increase background ensemble member size within the GSI-based EnVar system for improved radar analyses and forecasts of convective systems

A Valid Time Shifting (VTS) method is explored for the GSI-based ensemble variational (EnVar) system modified to directly assimilate radar reflectivity at convective scales. VTS is a cost-efficient method to increase ensemble size by including subensembles before and after the central analysis time. Additionally, VTS addresses common time and phase model error uncertainties within the ensemble. VTS is examined here for assimilating radar reflectivity in a continuous hourly analysis system for a case study of 1-2 May 2019. The VTS implementation is compared against a 36-member control experiment (ENS-36), to increase ensemble size (3×36 VTS), and as a cost-savings method (3×12 VTS), with time-shifting intervals τ between 15 and 120 min. The 3×36 VTS experiments increased the ensemble spread, with largest subjective benefits in early cycle analyses during convective development. The 3×12 VTS experiments captured analysis with similar accuracy as ENS-36 by the third hourly analysis. Control forecasts launched from hourly EnVar analyses show significant skill increases in 1-h precipitation over ENS-36 out to hour 12 for 3×36 VTS experiments, subjectively attributable to more accurate placement of the convective line. For 3×12 VTS, experiments with τ ≥ 60 min met and exceeded the skill of ENS-36 out to forecast hour 15, with VTS-3×12τ90 maximizing skill. Sensitivity results demonstrate preference to τ = 30–60 min for 3x36 VTS and 60 – 120 min for 3×12 VTS. The best 3×36 VTS experiments add a computational cost of 45-67%, compared to the near tripling of costs when directly increasing ensemble size, while best 3×12 VTS experiments save about 24-41% costs over ENS-36.

Assessing the Forecast Impact of a Geostationary Microwave Sounder using Regional and Global OSSEs

Forecast observing system simulation experiments (OSSEs) are conducted to assess the potential impact of geostationary microwave (GeoMW) sounder observations on numerical weather prediction forecasts. A regional OSSE is conducted using a tropical cyclone (TC) case that is very similar to hurricane Harvey (2017), as hurricanes are among the most devastating of weather-related natural disasters, and hurricane intensity continues to pose a significant challenge for numerical weather prediction. A global OSSE is conducted to assess the potential impact of a single GeoMW sounder centered over the continental United States versus two sounders positioned at the current locations of the National Oceanic and Atmospheric Administration Geostationary Operational Environmental Satellites (GOES) East and West. It is found that assimilation of GeoMW soundings result in better characterization of the TC environment, especially before and during intensification, which leads to significant improvements in forecasts of TC track and intensity. TC vertical structure (warm core thermal perturbation and horizontal wind distribution) is also substantially improved, as are the surface wind and precipitation extremes. In the global OSSE, assimilation of GeoMW soundings leads to slight improvement globally and significant improvement regionally, with regional impact equal to or greater than nearly all other observation types.

GEFSv12 reforecast dataset for supporting subseasonal and hydrometeorological applications

For the newly implemented Global Ensemble Forecast System version 12 (GEFSv12), a 31-year (1989-2019) ensemble reforecast dataset has been generated at the National Centers for Environmental Prediction (NCEP). The reforecast system is based on NCEP’s Global Forecast System version 15.1 and GEFSv12, which uses the Finite Volume 3 dynamical core. The resolution of the forecast system is ∼25 km with 64 vertical hybrid levels. The Climate Forecast System (CFS) reanalysis and GEFSv12 reanalysis serve as initial conditions for the Phase 1 (1989–1999) and Phase 2 (2000–2019) reforecasts, respectively. The perturbations were produced using breeding vectors and ensemble transforms with a rescaling technique for Phase 1 and ensemble Kalman filter 6-h forecasts for Phase 2. The reforecasts were initialized at 0000 (0300) UTC once per day out to 16 days with 5 ensemble members for Phase 1 (Phase 2), except on Wednesdays when the integrations were extended to 35 days with 11 members. The reforecast data set was produced on NOAA’s Weather and Climate Operational Supercomputing System at NCEP. This study summarizes the configuration and dataset of the GEFSv12 reforecast and presents some preliminary evaluations of 500hPa geopotential height, tropical storm track, precipitation, 2-meter temperature, and MJO forecasts. The results were also compared with GEFSv10 or GEFS Subseasonal Experiment reforecasts. In addition to supporting calibration and validation for the National Water Center, NCEP Climate Prediction Center, and other National Weather Service stakeholders, this high-resolution subseasonal dataset also serves as a useful tool for the broader research community in different applications.

Supercell thunderstorm charge structure variability and influences on spatial lightning flash relationships with the updraft

Relationships between lightning flashes and thunderstorm kinematics and microphysics are important for applications such as nowcasting of convective intensity. These relationships are influenced by cloud electrification structures and have been shown to vary in anomalously electrified thunderstorms. This study addresses transitional relationships between active charge structure and lightning flash location in the context of kinematic and microphysical updraft characteristics during the development of an anomalously electrified supercell thunderstorm in the Tennessee Valley on 10 April 2009. The initial charge structure within the updraft was characterized as an anomalous dipole in which positive charge was inferred in regions of precipitation ice (i.e., graupel and hail) and negative charge was inferred in regions of cloud ice (i.e., aggregates and ice crystals). During subsequent development of the anomalous charge structure, additional minor charge layers as well as evidence of increasing horizontal complexity were observed. Microphysical and kinematic characteristics of the charge structure also evolved to include increasing observations of negative charge in precipitation ice regions, indicating the emergence of more prominent normal charging alongside dominant anomalous charging. Simultaneously, lightning flash initiation locations were also increasingly observed in regions of faster updrafts and stronger horizontal gradients in updraft speed. It is suggested that continuous variability in charging behavior over meso-gamma spatial scales influenced the evolution of lightning flash locations with respect to the updraft structure. Further work is necessary to determine how this variability may impact lightning flash relation-ships, including lightning flash rate, with bulk microphysical and kinematic characteristics and related applications.

The Evolution of Asymmetries in the Tropical Cyclone Boundary Layer Wind Field during Landfall

The evolution of the tropical cyclone boundary layer (TCBL) wind field before landfall is examined in this study. As noted in previous studies, a typical TCBL wind structure over the ocean features a supergradient boundary layer jet to the left of motion and Earth-relative maximum winds to the right. However, the detailed response of the wind field to frictional convergence at the coastline is less well known. Here, idealized numerical simulations reveal an increase in the offshore radial and vertical velocities beginning once the TC is roughly 200 km offshore. This increase in the radial velocity is attributed to the sudden decrease in frictional stress once the highly agradient flow crosses the offshore coastline. Enhanced advection of angular momentum by the secondary circulation forces a strengthening of the supergradient jet near the top of the TCBL. Sensitivity experiments reveal that the coastal roughness discontinuity dominates the friction asymmetry due to motion. Additionally, increasing the inland roughness through increasing the aerodynamic roughness length enhances the observed asymmetries. Lastly, a brief analysis of in-situ surface wind data collected during the landfall of three Gulf of Mexico hurricanes is provided and compared to the idealized simulations. Despite the limited in-situ data, the observations generally support the simulations. The results here imply that assumptions about the TCBL wind field based on observations from over horizontally-homogeneous surface types - which have been well-documented by previous studies - are inappropriate for use near strong frictional heterogeneity.

Impacts of Coastal Terrain on Warm-Sector Heavy-Rain-Producing MCSs in Southern China

Warm-sector heavy rainfall in southern China refers to the heavy rainfall that occurs within a weakly-forced synoptic environment under the influence of monsoonal airflows. It is usually located near the southern coast, and is characterized by poor predictability and a close relationship with coastal terrain. This study investigates the impacts of coastal terrain on the initiation, organization and heavy-rainfall potential of MCSs in warm-sector heavy rainfall over southern China using quasi-idealized WRF simulations and terrain-modification experiments. Typical warm-sector heavy rainfall events were selected to produce composite environments that forced the simulations. MCSs in these events all initiated in the early morning and developed into quasi-linear convective systems along the coast with a prominent backbuilding process. When the small coastal terrain is removed, the maximum 12-h rainfall accumulation decreases by ~46%. The convection initiation is advanced ~2 h with the help of orographic lifting associated with flow interaction with the coastal hills in the control experiment. Moreover, the coastal terrain weakens near-surface winds and thus decreases the deep-layer vertical wind shear component perpendicular to the coast and increases the component parallel to the coast; the coastal terrain also concentrates the moisture and instability over the coastal region by weakening the boundary layer jet. These modifications lead to faster upscale growth of convection and eventually a well-organized MCS. The coastal terrain is beneficial for backbuilding convection and thus persistent rainfall by providing orographic lifting for new cells on the western end of the MCS, and by facilitating a stronger and more stagnant cold pool, which stimulates new cells near its rear edge.

Post-Processing Ensemble Weather Forecasts for Introducing Multisite and Multivariable Correlations using Rank Shuffle and Copula Theory

Statistical methods have been widely used to post-process ensemble weather forecasts for hydrological predictions. However, most of the statistical post-processing methods apply to a single weather variable at a single location, thus neglecting the inter-site and inter-variable dependence structures of forecast variables. This study synthesized a multisite and multivariate (MSMV) post-processing framework that extends the univariate method to the MSMV version by directly rearranging the post-processed ensemble members (post-reordering strategy) or by rearranging the latent variables used in univariate method (pre-reordering strategy). Based on the univariate Generator-based Post-Processing (GPP) method, the two reordering strategies and three dependence reconstruction methods (Rank shuffle (RS), Gaussian Copula (GC), and Empirical Copula (EC)) totaling 6 MSMV methods (RS-Pre, GC-Pre, EC-Pre, RS-Post, GC-Post, and EC-Post) were evaluated in post-processing ensemble precipitation and temperature forecasts for the Xiangjiang Basin in China using the 11-member ensemble forecasts from the Global Ensemble Forecasting System (GEFS). The results showed that raw GEFS forecasts tend to be biased for both the forecast ensembles and the inter-site and inter-variable dependencies. Univariate method can improve the univariate performance of ensemble mean and spread but misrepresent the inter-site and inter-variable dependence among the forecast variables. The MSMV framework can well utilize the advantages of the univariate method and also reconstruct the inter-site and inter-variable dependencies. Among the six methods, RS-Pre, RS-Post, GC-Post, and EC-Post perform better than the others with respect to reproducing the univariate statistics and multivariable dependences. The post-reordering strategy is recommended to combine the univariate method (i.e. GPP) and reconstruction methods.