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A novel high-resolution regional reanalysis is used to investigate the mesoscale processes that preceded the formation of Tropical Cyclone (TC) Mora (2017). Both satellite observations and the regional reanalysis show early morning mesoscale convective systems (MCSs) persistently initiated and organized in the downshear quadrant of the preexisting tropical disturbance a few days prior to the genesis of TC Mora. The diurnal MCSs gradually enhanced the meso-α-scale vortex near the center of the preexisting tropical disturbance through vortex stretching, providing a vorticity-rich and moist environment for the following burst of deep convection and enhancement of the meso-β-scale vortex. The regional reanalysis shows that the gravity waves that radiated from afternoon convection over the northern coast of the Bay of Bengal might play an important role in modulating the diurnal cycle of pregenesis MCSs. The diurnal convectively forced gravity waves increased the tropospheric stability, reduced the column saturation fraction, and suppressed deep convection within the preexisting tropical disturbance from noon to evening. A similar quasi-diurnal cycle of organized deep convection prior to TC genesis has also been observed over other basins. However, modeling studies are needed to conclusively demonstrate the relationships between the gravity waves and pregenesis diurnal MCSs. Also, whether diurnal gravity waves play a similar role in modulating the pregenesis deep convection in other TCs is worth future investigations. Significance Statement Tropical cyclogenesis is a process by which a less organized weather system in the tropics develops into a tropical cyclone (TC). Observations indicate that thunderstorms occurring prior to the tropical cyclogenesis often show a distinct quasi-diurnal cycle, while the related physical mechanisms are still unclear. In this study, we used a novel high-resolution dataset to investigate the diurnal thunderstorms occurring prior to the genesis of TC Mora (2017). We find that the pregenesis diurnal thunderstorms played a crucial role in spinning up the circulation of the atmosphere and provided a favorable environment for the rapid formation of Mora. It is likely that gravity waves emitted by afternoon thunderstorms over the inland region were responsible for regulating the diurnal variation of pregenesis thunderstorms over the ocean.

The assimilation of satellite all-sky infrared (IR) brightness temperatures (BTs) has been shown in previous studies to improve intensity forecasts of tropical cyclones. In this study, we examine whether assimilating all-sky IR BTs can also potentially improve tropical cyclogenesis forecasts by improving the pregenesis cloud and moisture fields. By using an ensemble-based data assimilation system, we show that the assimilation of upper-tropospheric water vapor channel BTs observed by the Meteosat-10 SEVIRI instrument two days before the formation of a tropical depression improves the genesis forecast of Hurricane Irma (2017), a classic Cape Verde storm, by up to 24 h while also capturing its later rapid intensification in deterministic forecasts. In an experiment that withholds the assimilation of all-sky IR BTs, the assimilation of conventional observations from the Global Telecommunications System (GTS) leads to the premature genesis of Hurricane Irma by at least 24 h. This premature genesis is shown to result from an overestimation of the spatial coverage of deep convection within the African easterly wave (AEW) from which Irma eventually forms. The gross overestimation of deep convection without all-sky IR BTs is accompanied by higher column saturation fraction, stronger low-level convergence, and the earlier spinup of a low-level meso-β-scale vortex within the AEW that ultimately becomes Hurricane Irma. Through its adjustment to the initial moisture and cloud conditions, the assimilation of all-sky IR BTs leads to a more realistic convective evolution in forecasts and ultimately a more realistic timing of genesis. Significance Statement Every year hurricanes impact the lives of thousands of people living along the eastern coast of the United States. Many of these storms originate from tropical disturbances that exit the west coast of Africa. To give the public more warning time ahead of these storms, it is important to improve the forecasts of their formation. This study uses a system developed at The Pennsylvania State University to incorporate satellite observations into forecasts of a classic Cape Verde storm, Hurricane Irma (2017), two days before it formed. By using satellite-collected radiances, we improve the timing of its formation by up to 24 h due to a better representation of the mesoscale tropical disturbance from which it originated.

The Prediction of Rainfall Extremes Campaign In the Pacific (PRECIP) aims to improve our understanding of extreme rainfall processes in the East Asian summer monsoon. A convection-permitting ensemble-based data assimilation and forecast system (the PSU WRF-EnKF system) was run in real time in the summers of 2020–21 in advance of the 2022 field campaign, assimilating all-sky infrared (IR) radiances from the geostationary Himawari-8 and GOES-16 satellites, and providing 48-h ensemble forecasts every day for weather briefings and discussions. This is the first time that all-sky IR data assimilation has been performed in a real-time forecast system at a convection-permitting resolution for several seasons. Compared with retrospective forecasts that exclude all-sky IR radiances, rainfall predictions are statistically significantly improved out to at least 4–6 h for the real-time forecasts, which is comparable to the time scale of improvements gained from assimilating observations from the dense ground-based Doppler weather radars. The assimilation of all-sky IR radiances also reduced the forecast errors of large-scale environments and helped to maintain a more reasonable ensemble spread compared with the counterpart experiments that did not assimilate all-sky IR radiances. The results indicate strong potential for improving routine short-term quantitative precipitation forecasts using these high-spatiotemporal-resolution satellite observations in the future. Significance Statement During the summers of 2020/21, the PSU WRF-EnKF data assimilation and forecast system was run in real time in advance of the 2022 Prediction of Rainfall Extremes Campaign In the Pacific (PRECIP), assimilating all-sky (clear-sky and cloudy) infrared radiances from geostationary satellites into a numerical weather prediction model and providing ensemble forecasts. This study presents the first-of-its-kind systematic evaluation of the impacts of assimilating all-sky infrared radiances on short-term qualitative precipitation forecasts using multiyear, multiregion, real-time ensemble forecasts. Results suggest that rainfall forecasts are improved out to at least 4–6 h with the assimilation of all-sky infrared radiances, comparable to the influence of assimilating radar observations, with benefits in forecasting large-scale environments and representing atmospheric uncertainties as well.

Mesoscale convective systems (MCSs) are the dominant rainfall producer over the U.S. during the warm season, causing natural disasters and severe weather every year. Global climate models have large uncertainty in projecting precipitation changes in the future climate. Here a simple Lagrangian parcel model is used to investigate the impact of global warming on MCS initiation and growth. The single-column parcel model projects a mean precipitation decrease over the central U.S. and an increase to its east, in agreement with the CMIP5 model projection. It also highlights the crucial role of current climate mean state model bias in exaggerating the change in future mean precipitation projection by 25%. As for convective population, the model captures the decreased occurrence frequency of weak to moderate convection and increased frequency of strong convection due to the increased CAPE and CIN, in agreement with convection-permitting regional simulations. Novel parameterizations of gust front propagation speed and subsidence strength are developed as guided by cloud-resolving simulations. The multi-column parcel model employing those parameterizations captures readily the cold pool-induced upscale growth feature. It simulates smaller mesoscale clusters over the central U.S. under global warming due to gust front slowdown and subsidence strength enhancement, which is further attributed to land aridity-induced weakening of initial accumulated precipitation and strengthening of updraft speed, respectively. That said, mesoscale clusters could become bigger under more favorable conditions in future climate, including boundary layer moistening, convection life time lengthening, and cold pool mechanical lifting enhancement, which require further investigations to improve mechanistic understanding of future MCS changes.

Through a series of global convection-permitting simulations and geostationary satellite observations, this study investigates the role of deep moist convection in atmospheric kinetic energy (KE) and brightness temperature (BT) spectra in a realistic framework. The control simulation was produced on a quasi-uniform 3-km global mesh, which allowed the explicit representation of deep convection. To assess the impact of deep moist convection, a fake-dry simulation was performed with latent heating–cooling feedback in the microphysics removed for comparison. The impacts of deep moist convection on mesoscale KE spectrum are concentrated on energizing the mesoscale at the upper troposphere and the lower stratosphere through buoyancy production. BT spectra for the control simulation have a similar shallow slope in the mesoscale as that for the observations. The greater spectral power of BT for the control simulation compared to the observed is attributed to the dislocation and higher intensity of simulated convection. The observed BT spectra exhibit a large diurnal variability due to the diurnal variation of the intensity of convection. The simulated BT spectrum is dependent on convective systems at different scales. Deep convection in the intertropical convergence zone (ITCZ) and shallow convection in the North Pacific storm-track region play an important role in energizing the convective scale of the BT spectrum. In the mesoscale, the BT spectrum is mainly energized by mesoscale convective systems (MCSs) in the ITCZ. Tropical equatorial waves and baroclinic waves in the southern midlatitudes are critical in producing the shallow slope near −5/3 and providing energy in the BT spectrum at the synoptic scale. Significance Statement We further explore the role of deep moist convection in kinetic energy and brightness temperature spectra through high-resolution radiance observations and convection-permitting simulations. Moist processes can energize the mesoscale of kinetic energy. Brightness temperature spectra show dependence on convective systems at different scales. These results point the way toward a new approach to evaluate the predictability of convective systems, and future development of model dynamics and parameterization.

Convective vertical transport is critical in the monsoonal overturning, but the relative roles of different convective systems are not well understood. This study used a cloud classification and tracking technique to decompose a convection-permitting simulation of the South Asian summer monsoon (SASM) into subregimes of mesoscale convective systems (MCSs), non-MCS deep convection (non-MCS), congestus, and shallow convection/clear sky. Isentropic analysis is adopted to quantify the contributions of different convective systems to the total SASM vertical mass, water, and energy transports. The results underscore the crucial roles of MCSs in the SASM vertical transports. Compared to non-MCSs, the total mass and energy transports by MCSs are at least 1.5 times stronger throughout the troposphere, with a larger contributing fraction from convective updrafts compared to upward motion in stratiform regions. Occurrence frequency of non-MCSs is around 40 times higher than that of MCSs. However, per instantaneous convection features, the vertical transports and net moist static energy (MSE) exported by MCSs are about 70–100 and 58 times stronger than that of non-MCSs. While these differences are dominantly contributed by differences in the per-feature MCS and non-MCS area coverage, MCSs also show stronger transport intensities than non-MCSs over both ocean and land. Oceanic MCSs and non-MCSs show more obvious top-heavy structures than their inland counterparts, which are closely related to the widespread stratiform over ocean. Compared to the monsoon break phase, MCSs occur more frequently (~1.6 times) but their vertical transport intensity slightly weakens (by ~10%) during the active phases. These results are useful for understanding the SASM and advancing the energetic framework.

Over the course of his career, Fuqing Zhang drew vital new insights into the dynamics of meteorologically significant mesoscale gravity waves (MGWs), including their generation by unbalanced jet streaks, their interaction with fronts and organized precipitation, and their importance in midlatitude weather and predictability. Zhang was the first to deeply examine “spontaneous balance adjustment”—the process by which MGWs are continuously emitted as baroclinic growth drives the upper-level flow out of balance. Through his pioneering numerical model investigation of the large-amplitude MGW event of 4 January 1994, he additionally demonstrated the critical role of MGW–moist convection interaction in wave amplification. Zhang’s curiosity-turned-passion in atmospheric science covered a vast range of topics and led to the birth of new branches of research in mesoscale meteorology and numerical weather prediction. Yet, it was his earliest studies into midlatitude MGWs and their significant impacts on hazardous weather that first inspired him. Such MGWs serve as the focus of this review, wherein we seek to pay tribute to his groundbreaking contributions, review our current understanding, and highlight critical open science issues. Chief among such issues is the nature of MGW amplification through feedback with moist convection, which continues to elude a complete understanding. The pressing nature of this subject is underscored by the continued failure of operational numerical forecast models to adequately predict most large-amplitude MGW events. Further research into such issues therefore presents a valuable opportunity to improve the understanding and forecasting of this high-impact weather phenomenon, and in turn, to preserve the spirit of Zhang’s dedication to this subject.

Our study intends to explore the effect of bone marrow mesenchymal stem cells (BMSCs) on neuronal activity and GDNF expression in rats with SCI. Rats were assigned into spinal cord injury group (group S); routinely control group (group R) without spinal incision; and BMSCs group (group B) which received BMSCs treatment with n = 4 rats in each group. The neuronal cell proliferation was detected by cck-8 method, BBB score was used to detect lower limb motor function, GDNF mRNA expression was detected by qRT-PCR, GDNF protein positive expression was measured by immunohistochemistry and cell invasion was assessed by Transwell. Group B rats showed significantly higher BBB score higher than group S (P P P P P P P P P P

The advent of modern geostationary satellite infrared radiance observations has noticeably improved numerical weather forecasts and analyses. However, compared to midlatitude weather systems and tropical cyclones, research into using infrared radiance observations for numerically predicting and analyzing tropical mesoscale convective systems remain mostly fallow. Since tropical mesoscale convective systems play a crucial role in regional and global weather, this deficit should be addressed. This study is the first of its kind to examine the potential impacts of assimilating all-sky upper tropospheric infrared radiance observations on the prediction of a tropical squall line. Even though these all-sky infrared radiance observations are not directly affected by lower-tropospheric winds, the high-frequency assimilation of these all-sky infrared radiance observations improved the analyses of the tropical squall line’s outflow position. Aside from that, the assimilation of all-sky infrared radiance observations improved the analyses and prediction of the squall line’s cloud field. Finally, reducing the frequency of assimilating these all-sky infrared radiance observations weakened these improvements to the analyzed outflow position, as well as the analyses and predictions of cloud fields.

The critical velocity is the minimum velocity blowing towards the fire seat in tunnels to reduce the length of back-layering to zero, and it has been extensively adopted in the design of longitudinal smoke control in tunnels. A new concept, i.e., sub-critical velocity, is proposed in lieu of critical velocity for longitudinally ventilated tunnels. The sub-critical velocity allows the presence of back-layering upstream of the fire, provided that the back-layering is kept above the heads of motorists, thus posing no detrimental impact on them during evacuation. Experiments with propane fires with heat release rates of 3.7–25.5 kW were conducted in a 1/20 reduced-scale model tunnel. Both the lengths and the heights of back-layering at varying HRRs and longitudinal velocities were studied. Subsequently, the relationship of critical velocity and sub-critical velocity were developed. The results showed that sub-critical velocity is around 45–60% of the critical velocity. The adoption of sub-critical velocity in the design of longitudinally ventilated tunnels not only significantly reduces the cost of fans to drive the airflow, but also minimizes the impact of ventilation on heat release rates.

The Tibetan Plateau and its surrounding mountains have an average elevation of 4,400 m and a glaciated area of ~100,000 km 2 giving it the name "Third Pole (TP) region". The TP is the headwater of many major rivers in Asia that provide fresh water to hundreds of millions of people. Climate change is altering the energy and water cycle of the TP at a record pace but the future of this region is highly uncertain due to major challenges in simulating weather and climate processes in this complex area. The Convection-Permitting Third Pole (CPTP) project is a Coordinated Regional Downscaling Experiment (CORDEX) Flagship Pilot Study (FPS) that aims to revolutionize our understanding of climate change impacts on the TP through ensemble-based, kilometer-scale climate modeling. Here we present the experimental design and first results from multi-model, multi-physics ensemble simulations of three case studies. The five participating modeling systems show high performance across a range of meteorological situations and are close to having ''observational quality'' in simulating precipitation and near-surface temperature. This is partly due to the large differences between observational datasets in this region, which are the leading source of uncertainty in model evaluations. However, a systematic cold bias above 2,000\,m exists in most modeling systems. Model physics sensitivity tests performed with the Weather Research and Forecasting (WRF) model show that planetary boundary layer (PBL) physics and microphysics contribute equally to model uncertainties. Additionally, larger domains result in better model performance. We conclude by describing high-priority research needs and the next steps in the CPTP project.

The South China coast suffers frequent heavy rainfall every warm season. Based on the objective classification method of principal components analysis, the key role of the synoptic pattern in determining the heavy rainfall processes that occurred over the South China coast in the warm season during 2008–18 is examined in this study. We found that heavy rainfall occurs most frequently under three typical synoptic patterns (P1–P3 hereafter) characterized by strong low-level onshore winds. P1 and P3 feature a prevailing southwesterly monsoonal flow in the lower troposphere, with heavy rainfall frequently occurring over the inland windward region in the afternoon associated with the orographic lifting and solar heating. The onshore wind of P3 is stronger than P1 as the western Pacific subtropical high extends more westward to 122°E, which induces stronger low-level convergence along the coastline than P1 when the ageostrophic wind veers from the offshore to onshore direction in the early morning. Hence, a secondary early morning rainfall peak can be found along the coastline. P2 is characterized by a low-level vortex located over the southwest portion of south China. Heavy rainfall under P2 usually initiates over the western part of the coastal region in the morning and then propagates inland in the afternoon. Overall, the synoptic patterns strongly determine the spatial distribution and diurnal cycle of heavy rainfall over the South China coast. This heavy rainfall is closely related to the diurnally varying low-level onshore winds rather than the low-level jets, as well as the different interactions between the low-level onshore winds and the local orography, coastline, and land–sea breeze circulations under different synoptic patterns.

Cloud microphysics significantly impact tropical cyclone precipitation. A prior polarimetric radar observational study by the authors revealed the ice-phase microphysical processes as the dominant microphysics mechanisms responsible for the heavy precipitation in the outer rainband of Typhoon Nida (2016). To assess the model performance regarding microphysics, three double-moment microphysics schemes (i.e., Thompson, Morrison, and WDM6) are evaluated by performing a set of simulations of the same case. While these simulations capture the outer rainband’s general structure, microphysics in the outer rainbands are strikingly different from the observations. This discrepancy is primarily attributed to different microphysics parameterizations in these schemes, rather than the differences in large-scale environments due to cloud–environment interactions. An interesting finding in this study is that the surface rain rate or liquid water content is inversely proportional to the simulated mean raindrop sizes. The mass-weighted raindrop diameters are overestimated in the Morrison and Thompson schemes and underestimated in the WDM6 scheme, while the former two schemes produce lower liquid water content than WDM6. Compared with the observed ice water content based on a new polarimetric radar retrieval method, the ice water content above the environmental 0°C level in all simulations is highly underestimated, especially at heights above 12 km MSL where large concentrations of small ice particles are typically prevalent. This finding suggests that the improper treatment of ice-phase processes is potentially an important error source in these microphysics schemes. Another error source identified in the WDM6 scheme is overactive warm-rain processes that produce excessive concentrations of smaller raindrops.

Understanding the dynamics of the flow-dependent forecast error covariance across the air–sea interface is beneficial toward revealing the potential influences of strongly coupled data assimilation on tropical cyclone (TC) initialization in coupled models, and the fundamental dynamics associated with TC air–sea interactions. A 200-member ensemble of convection-permitting forecasts from a coupled atmosphere–ocean regional model is used to investigate the forecast error covariance across the oceanic and atmospheric domains during the rapid intensification of Hurricane Florence (2018). Forecast uncertainties in both atmospheric and oceanic domains, from an Eulerian perspective, increase with forecast lead time, mainly from TC displacement errors. In a storm-relative framework, the ensemble forecast uncertainties in both domains are predominantly caused by differences in the simulated storm intensity and structure. The largest ensemble spread in the atmospheric pressure, temperature, and wind fields can be found within the TC inner-core region. Alternatively, the largest ensemble spread in the upper-ocean currents and temperature fields are located along the cold wake behind the storm. Cross-domain ensemble correlations between simulated atmospheric (oceanic) observations and oceanic (atmospheric) state variables in the storm-relative coordinates are highly anisotropic, variable dependent, and ultimately driven by the dynamics of TC air–sea interactions. Meaningful and dynamically consistent cross-domain ensemble correlations suggest that it is possible to use atmospheric and oceanic observations to simultaneously update state variables associated with the coupled ocean–atmosphere prediction of TCs using strongly coupled data assimilation. Sensitivity experiments demonstrate that at least 60–80 ensemble members are required to represent physically consistent cross-domain correlations and minimize sampling errors.

The introduction of infrared water vapor channel radiance ensemble data assimilation (DA) has improved numerical weather forecasting at operational centers. Further improvements might be possible through extending ensemble data assimilation methods to better assimilate infrared satellite radiances. Here, we will illustrate that ensemble statistics under clear-sky conditions are different from cloudy conditions. This difference suggests that extending the ensemble Kalman filter (EnKF) to handle bi-Gaussian prior distributions may yield better results than the standard EnKF. In this study, we propose a computationally efficient bi-Gaussian ensemble Kalman filter (BGEnKF) to handle bi-Gaussian prior distributions. As a proof-of-concept, we used the 40-variable Lorenz 1996 model as a proxy to examine the impacts of assimilating infrared radiances with the BGEnKF and EnKF. A nonlinear observation operator that constructs radiance-like bimodal ensemble statistics was used to generate and assimilate pseudoradiances. Inflation was required for both methods to effectively assimilate pseudoradiances. In both 800- and 20-member experiments, the BGEnKF generally outperformed the EnKF. The relative performance of the BGEnKF with respect to the EnKF improved when the observation spacing and time between DA cycles (cycling interval) are increased from small values. The relative performance then degraded when observation spacing and cycling interval become sufficiently large. The BGEnKF generated less noise than the EnKF, suggesting that the BGEnKF produces more balanced analysis states than the EnKF. This proof-of-concept study motivates future investigation into using the BGEnKF to assimilate infrared observations into high-order numerical weather models.

Geostationary infrared satellite observations are spatially dense [>1/(20 km)2] and temporally frequent (>1 h−1). These suggest the possibility of using these observations to constrain subsynoptic features over data-sparse regions, such as tropical oceans. In this study, the potential impacts of assimilating water vapor channel brightness temperature (WV-BT) observations from the geostationary Meteorological Satellite 7 (Meteosat-7) on tropical convection analysis and prediction were systematically examined through a series of ensemble data assimilation experiments. WV-BT observations were assimilated hourly into convection-permitting ensembles using Penn State’s ensemble square root filter (EnSRF). Comparisons against the independently observed Meteosat-7 window channel brightness temperature (Window-BT) show that the assimilation of WV-BT generally improved the intensities and locations of large-scale cloud patterns at spatial scales larger than 100 km. However, comparisons against independent soundings indicate that the EnSRF analysis produced a much stronger dry bias than the no data assimilation experiment. This strong dry bias is associated with the use of the simulated WV-BT from the prior mean during the EnSRF analysis step. A stochastic variant of the ensemble Kalman filter (NoMeanSF) is proposed. The NoMeanSF algorithm was able to assimilate the WV-BT without causing such a strong dry bias and the quality of the analyses’ horizontal cloud pattern is similar to EnSRF’s analyses. Finally, deterministic forecasts initiated from the NoMeanSF analyses possess better horizontal cloud patterns above 500 km than those of the EnSRF. These results suggest that it might be better to assimilate all-sky WV-BT through the NoMeanSF algorithm than the EnSRF algorithm.

Using 5 years of operational Doppler radar, cloud-to-ground (CG) lightning observations, and National Centers for Environmental Prediction reanalysis data, this study examined the spatial and temporal characteristics of and correlations between summer storm and lightning activity over the Yangtze River Delta (YRD), with a focus on subseasonal variability and diurnal cycles. The spatiotemporal features of storm top, duration, maximum reflectivity, size, and cell-based vertical integrated liquid water were investigated using the Storm Cell Identification and Tracking algorithm. Our results revealed that there was high storm activity over the YRD, with weak diurnal variations during the mei-yu period. Specifically, storms were strongly associated with mei-yu fronts and exhibited a moderate size, duration, and intensity. On average, afternoon storms exhibited stronger reflectivity and higher storm tops than morning storms, as evidenced by the afternoon peak in CG lightning. The storm intensity strengthened in the post-mei-yu period, due to an increase in atmospheric instability; this was accompanied by a higher frequency of CG lighting. The diurnal cycles of storm frequency and CG lightning in the post-mei-yu period followed a unimodal pattern with an afternoon peak. This is attributable to increased thermodynamic instability in the afternoon, as little diurnal variation was observed for wind shear and moisture. An inverse correlation between lightning occurrence and mean peak current (MPC) for negative CG lightning was evident during the pre-mei-yu and mei-yu periods. The diurnal variation in MPC for negative CG lightning agreed well with that for storm intensity.

A regional‐scale fully coupled data assimilation (DA) system based on the ensemble Kalman filter is developed for a high‐resolution coupled atmosphere‐ocean model. Through the flow‐dependent covariance both within and across the oceanic and atmospheric domains, the fully coupled DA system is capable of updating both atmospheric and oceanic state variables simultaneously by assimilating either atmospheric and/or oceanic observations. The potential impacts of oceanic observations, including sea‐surface temperature, sea‐surface height anomaly, and sea‐surface current, in addition to the observation of the minimum surface pressure at the storm center (HPI), on tropical cyclone analysis and prediction are examined through observing system simulation experiments of Hurricane Florence (2018). Results show that assimilation of oceanic observations not only resulted in better analysis and forecast of the oceanic variables but also considerably reduced analysis and forecast errors in the atmospheric fields, including the intensity and structure of Florence. Compared to weakly coupled DA in which the analysis update is performed separately for the atmospheric and oceanic domains, fully coupled DA reduces the forecast errors of tropical cyclone track and intensity. Results show promise in potential further improvement in tropical cyclone prediction through assimilation of both atmospheric and oceanic observations using the ensemble‐based fully coupled DA system.

Ensemble-based data assimilation of radar observations across inner-core regions of tropical cyclones (TCs) in tandem with satellite all-sky infrared radiances across the TC domain improves TC trac...

Mesoscale convective systems (MCSs) account for more than 50% of summer-time precipitation over the central United States (US) and have a significant impact on local weather and hydrologic cycle. It is hypothesized that the inadequate treatment of MCSs is responsible for the longstanding warm and dry bias over the central US in coarse-resolution general circulation model (GCM) simulations. In particular, a better understanding of MCS initiation is still lacking. Here a single-column Lagrangian parcel model is first developed to simulate the basic features of a rising parcel. This simple model demonstrates the collective effects of boundary layer moistening and dynamical lifting in triggering convective initiation and reproduces successfully its early afternoon peak with surface equivalent potential temperature as a controlling factor. It also predicts that convection is harder to trigger in the future climate under global warming, consistent with the results from convection-permitting regional climate simulations. Then a multi-column model that includes an array of single-column models aligned in the east-west direction and incorporates idealized cold pool interaction mechanisms is developed. The multi-column model captures readily the cold pool induced upscale growth feature in MCS genesis from initially scattered convection that is organized into a mesoscale cluster in a few hours. It also highlights the crucial role of lifting effects due to cold pool collision and spreading, subsidence effect, and gust front propagation speed in controlling the final size of mesoscale clusters and cold pool regions. This simple model should be useful for understanding fundamental mechanisms of MCS initiation and providing guidance for improving MCS simulations in GCMs.

There are ongoing efforts to establish an ensemble data assimilation and prediction system for tropical cyclones based on the FV3 (finite-volume cubed-sphere) dynamic core with the capability to assimilate satellite all-sky infrared and microwave observations. To complement the system developments and improve our understanding of the assimilation of all-sky infrared and microwave observations, this study assesses their potential impacts on the analysis of Hurricane Harvey (2017) through examinations of the structure and dynamics of the ensemble-based correlations as well as single observation data assimilation experiments, using an ensemble forecast generated by a global-to-regional nested FV3-based model. It is found that different infrared and microwave channels are sensitive to different types of hydrometeors within different layers of the atmosphere, and the correlations vanish beyond 200 km in the region covered by cloud or abundant hydrometeors. The spatial correlations between brightness temperatures and model states will adjust the structure and intensity of the hurricane in the model so that the simulated hurricane will better fit the “observed” brightness temperatures. In general, these results show how assimilating infrared and microwave together can improve the analyses of tropical cyclone intensity and structure, which may lead to improved intensity forecasts.

Recent studies have shown that the assimilation of all-sky infrared (IR) observations can be beneficial for tropical cyclone analyses and predictions. The assimilation of Tail Doppler Radar (TDR) radial velocity observations has also been shown to improve tropical cyclone analyses and predictions; however, there is a paucity of literature on the impacts of simultaneously assimilating them with all-sky infrared IR brightness temperatures (BTs). This study examines the impacts of assimilating combinations of GOES-16 all-sky IR brightness temperatures, NOAA P-3 TDR radial velocities, and conventional observations from the Global Telecommunications System (GTS) on the analyses and forecasts of Hurricane Dorian (2019). It is shown that including IR and/or TDR observations on top of conventional GTS observations significantly reduces both track and intensity forecast errors. Track errors are reduced the most (25% at lead times greater than 48 h) when TDR and GTS observations are assimilated. In terms of intensity, errors are always lower at lead times greater than 48 h when IR BTs are assimilated. Simultaneously assimilating TDR and IR observations has the potential to further improve the intensity forecast by as much as 37% at a lead time of 48 h to 72 h. The improved intensity forecasts produced by the experiments assimilating all three observation sources are shown to be a result of the competing effects of IR assimilation producing an overly broad area of strong cyclonic circulation and TDR assimilation constraining that circulation to a more realistic size and intensity. Interestingly, the order in which observations are assimilated has non-negligible impacts on the analyses and forecasts of Dorian.

The tropical cyclone (TC) surface-exchange coefficients of enthalpy (Ck) and momentum (Cd) at high wind speeds have been notoriously challenging to estimate. This difficulty arises from many factors, including the difficulties in collecting observations within the turbulent TC boundary layer, and the complex coupled physical interactions between the TC boundary layer and ocean surface, which are challenging to accurately model. Motivated by recent studies highlighting the limited practical predictability of TC intensity as a result of uncertainty in the physical representation of the air-sea fluxes of momentum and enthalpy at high wind speeds, we investigate the potential to estimate the surface enthalpy and momentum exchange coefficients through ensemble data assimilation. Significant ensemble correlations between tangential wind, radial wind, and simulated infrared brightness temperatures with parameters controlling the enthalpy and momentum exchange coefficients suggest potential to use all-sky satellite and/or airborne radial velocity observations to estimate these unknown parameters. Using a series of observing system simulation experiments (OSSEs), simulated infrared brightness temperature observations, and a known truth, we demonstrate some potential for simultaneous state and parameter estimation with an ensemble-based data assimilation system to converge toward the correct known parameter values. In all OSSEs with either one or multiple unknown parameters, the initial parameter errors are reduced through simultaneous model state and parameter estimation. However, challenges still exist in converging to the precise true parameter values, as state errors during rapid intensification can project onto the parameter estimates.

The Tibetan Plateau is regarded as the Earth's Third Pole, which is the source region of several major rivers that impact more 20% the world population. This high‐altitude region is reported to have been undergoing much greater rate of weather changes under global warming, but the existing reanalysis products are inadequate for depicting the state of the atmosphere, particularly with regard to the amount of precipitation and its diurnal cycle. An ensemble Kalman filter (EnKF) data assimilation system based on the limited‐area Weather Research and Forecasting (WRF) model was evaluated for use in developing a regional reanalysis over the Tibetan Plateau and the surrounding regions. A 3‐month prototype reanalysis over the summer months (June−August) of 2015 using WRF‐EnKF at a 30‐km grid spacing to assimilate nonradiance observations from the Global Telecommunications System was developed and evaluated against independent sounding and satellite observations in comparison to the ERA‐Interim and fifth European Centre for Medium‐Range Weather Forecasts Reanalysis (ERA5) global reanalysis. Results showed that both the posterior analysis and the subsequent 6‐ to 12‐hr WRF forecasts of the prototype regional reanalysis compared favorably with independent sounding observations, satellite‐based precipitation versus those from ERA‐Interim and ERA5 during the same period. In particular, the prototype regional reanalysis had clear advantages over the global reanalyses of ERA‐Interim and ERA5 in the analysis accuracy of atmospheric humidity, as well as in the subsequent downscale‐simulated precipitation intensity, spatial distribution, diurnal evolution, and extreme occurrence., Key Points A pilot reanalysis over the Tibetan Plateau using the PSU WRF‐EnKF system was developed and evaluated against independent soundings and satellite observations as well as ERA‐Interim and ERA5The pilot reanalysis had clear advantage in atmospheric humidity than existing global reanalysisThe subsequent downscaled precipitation forecasts from the pilot reanalysis compared favorably against those from the global reanalyses of ERA‐Interim and ERA5

Hurricane Harvey brought catastrophic destruction and historical flooding to the Gulf Coast region in late August 2017. Guided by numerical weather prediction models, operational forecasters at NOAA provided outstanding forecasts of Harvey’s future path and potential for record flooding days in advance. These forecasts were valuable to the public and emergency managers in protecting lives and property. The current study shows the potential for further improving Harvey’s analysis and prediction through advanced ensemble assimilation of high-spatiotemporal all-sky infrared radiances from the newly launched, next-generation geostationary weather satellite, GOES-16. Although findings from this single-event study should be further evaluated, the results highlight the potential improvement in hurricane prediction that is possible via sustained investment in advanced observing systems, such as those from weather satellites, comprehensive data assimilation methodologies that can more effectively ingest existing and future observations, higher-resolution weather prediction models with more accurate numerics and physics, and high-performance computing facilities that can perform advanced analysis and forecasting in a timely manner.