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The Korea Institute of Atmospheric Prediction Systems (KIAPS) has developed a new global numerical weather prediction model, named the Korean Integrated Model (KIM). This paper presents the cumulus parameterization scheme (CPS) used in KIM, which originates from the simplified Arakawa–Schubert (SAS) convection scheme in the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) and has undergone numerous modifications in an effort to improve the medium-range forecast skill for precipitation and large-scale fields. The modifications include the following: 1) the threshold of the trigger condition is updated to consider the dependency on the environmental relative humidity (RH) averaged over the subcloud layer in order to suppress the trigger of convection in dry low-level environments; 2) the entrainment rate is modified to increase the sensitivity to environmental humidity, so that enhanced entrainment under lower RH conditions leads to a greater decrease in the strength of the convection that develops in drier environments; 3) the autoconversion parameter from cloud condensate to convective precipitation is changed to have a temperature dependency above the freezing level; 4) the closure is modified to consider rapidly varying boundary layer forcing; 5) the effect of the convection-induced pressure gradient force in convective momentum transport is enhanced in the upper part of the convective updrafts; and 6) scale awareness that enables a mass-flux CPS to work seamlessly at various grid sizes across gray-zone resolutions is addressed. The evaluation of medium-range forecasts with the KIM CPS reveals higher forecast skill, especially over the tropics, in comparison with its original version. [ABSTRACT FROM AUTHOR]

A Winter Fog Experiment (WiFEX) was conducted to study the genesis of fog formation between winters 2016–17 and 2017–18 at Indira Gandhi International Airport (IGIA), Delhi, India. To support the WiFEX field campaign, the Weather Research and Forecasting (WRF) Model was used to produce real-time forecasts at 2-km horizontal grid spacing. This paper summarizes the performance of the model forecasts for 43 very dense fog episodes (visibility < 200 m) and preliminary evaluation of the model against the observations. Similarly, near-surface liquid water content (LWC) from models and continuous visibility observations are used as a metric for model evaluation. Results show that the skill score is relatively promising for the hit rate with a value of 0.78, whereas the false alarm rate (0.19) and missing rate (0.32) are quite low. This indicates that the model has reasonable predictive accuracy, and the performance of the real-time forecast is better for both dense fog events and no-fog events. For success cases, the model accurately captured the near-surface meteorological conditions, particularly the low-level moisture, wind fields, and temperature inversion. In contrast, for failed cases, the WRF Model shows large error in near-surface relative humidity and temperature compared to the observations, although it captures temperature inversions reasonably well. Our results also suggest that the model is able to capture the variability in fog onset for consecutive fog events. Errors in near-surface variables during failed cases are found to be affected by the errors in the initial conditions taken from the Indian Institute of Tropical Meteorology Global Forecasting System (IITM-GFS) spectral model forecast. Further evaluation of the operational forecasts for dense fog cases indicates that the error in predicting fog onset stage is relatively large (mean error of 4 h) compared to the dissipation stage. [ABSTRACT FROM AUTHOR]

Santa Ana winds, common to Southern California from the fall through early spring, are a type of downslope windstorm originating from a direction generally ranging from 360°/0° to 100° and are usually accompanied by very low humidity. Since fuel conditions tend to be driest from late September through the middle of November, Santa Ana winds occurring during this time have the greatest potential to produce large, devastating fires upon ignition. Such catastrophic fires occurred in 1993, 2003, 2007, and 2008. Because of the destructive nature of such fires, there has been a growing desire to categorize Santa Ana wind events in much the same way that tropical cyclones have been categorized. The Santa Ana wildfire threat index (SAWTI) is a tool for categorizing Santa Ana wind events with respect to anticipated fire potential. The latest version of the index has been a result of a three-and-a-half-year collaboration effort between the USDA Forest Service, the San Diego Gas and Electric utility (SDG&E), and the University of California, Los Angeles (UCLA). The SAWTI uses several meteorological and fuel moisture variables at 3-km resolution as input to the Weather Research and Forecasting (WRF) Model to generate the index out to 6 days. In addition to the index, a 30-yr climatology of weather, fuels, and the SAWTI has been developed to help put current and future events into perspective. This paper outlines the methodology for developing the SAWTI, including a discussion on the various datasets employed and its operational implementation. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Environments associated with severe hailstorms, compared to those of tornadoes, are often less apparent to forecasters. Understanding has evolved considerably in recent years; namely, that weak low-level shear and sufficient convective available potential energy (CAPE) above the freezing level is most favorable for large hail. However, this understanding comes only from examining the mean characteristics of large hail environments. How much variety exists within the kinematic and thermodynamic environments of large hail? Is there a balance between shear and CAPE analogous to that noted with tornadoes? We address these questions to move toward a more complete conceptual model. In this study, we investigate the environments of 92 323 hail reports (both severe and nonsevere) using ERA5 modeled proximity soundings. By employing a self-organizing map algorithm and subsetting these environments by a multitude of characteristics, we find that the conditions leading to large hail are highly variable, but three primary patterns emerge. First, hail growth depends on a favorable balance of CAPE, wind shear, and relative humidity, such that accounting for entrainment is important in parameter-based hail prediction. Second, hail growth is thwarted by strong low-level storm-relative winds, unless CAPE below the hail growth zone is weak. Finally, the maximum hail size possible in a given environment may be predictable by the depth of buoyancy, rather than CAPE itself. [ABSTRACT FROM AUTHOR]

Deep-layer vertical wind shear and midtropospheric relative humidity (RH) are explored in and around environments of all intensifying North Atlantic tropical cyclones (TCs) between 1980 and 2021 using reanalysis data. Shear and RH are averaged within the standard environmental annulus of 200–800 km, along with a 100–600-km annulus, and a 0–250-km radius to represent the inner core and TC itself. Distributions of shear and RH at onset along with a time series of evolution from 48 h prior to and after onset of three different intensification rates, slight [5–10 kt (24 h)−1; 1 kt ≈ 0.51 m s−1], moderate [15–25 kt (24 h)−1], and rapid [≥30 kt (24 h)−1], are analyzed. RH is also investigated within different shear environments and in shear-relative quadrants around the storm. While low shear and high RH are found to be most favorable for rapid intensification (RI), there is still a significant probability that RI will occur within less favorable environments. RI cases decrease in 850–200-hPa shear in the 24 h leading up to RI, whereas slight intensification cases increase, which is evident in both the standard shear and a shallower layer at 48 h prior to onset. The inner-core RH for RI increases prior to onset whereas it decreases in the environments. RH analysis by shear-relative quadrants demonstrates the importance of moistening in the upshear-right quadrant before onset of RI. Results indicate the potential value of multiple annuli and shear-relative analysis for moisture and a shallower, 925–400-hPa layer for shear in RI forecasting. Significance Statement: The purpose of this study was to investigate the wind and moisture around different areas of intensifying North Atlantic tropical cyclones between 1980 and 2021 using reanalysis data. Average wind and moisture evolve differently around the onset of different intensification rates as well as in different near and far regions from the storm center. These results provide additional indicators that forecasters may consider when examining how the environment around the storm and the situation in the inner core may influence its future intensity. [ABSTRACT FROM AUTHOR]

This paper presents an evaluation study of a real-time fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5) mesoscale ensemble prediction system in the Taiwan area during the 2003 mei-yu season. The ensemble system consists of 16 members that used the same nested domains of 45- and 15-km resolutions, but different model settings of the initial conditions (ICs), the cumulus parameterization scheme (CPS), and the microphysics scheme (MS). Verification of geopotential height, temperature, relative humidity, and winds in the 15-km grid shows that the members using the Kain–Fritsch CPS performed better than those using the Grell CPS, and those using the Central Weather Bureau (CWB) Nonhydrostatic Forecast System (NFS) ICs fared better than those using the CWB Global Forecast System (GFS) ICs. The members applying the mixed-phase MS generally exhibited the smallest errors among the four MSs. Precipitation verification shows that the members using the Grell CPS, in general, had higher equitable threat scores (ETSs) than those using the Kain–Fritsch CPS, that the members with the GFS ICs performed better than with the NFS ICs, and that the mixed-phase and Goddard MSs gave relatively high ETSs in the rainfall simulation. The bias scores show that, overall, all 16 members underforecasted rainfall. Comparisons of the ensemble means show that, on average, an ensemble mean, no matter how many members it contains, can produce better forecasts than an individual member. Among the three possible elements (IC, CPS, and MS) that can be varied to compose an ensemble, the ensemble that contains members with all three elements varying performed the best, while that with two elements varying was second best, and that with only one varying was the worst. Furthermore, the first choice for composing an ensemble is to use perturbed ICs, followed by the perturbed CPS, and then the perturbed MS. [ABSTRACT FROM AUTHOR]

This paper seeks to address some of the limitations in previous statistical forecast models of tropical cyclogenesis through the development of a series of Poisson regression models on a 2° latitude × 5° longitude spatial grid and a monthly grid in time. The “Gray” parameters [low-level relative vorticity, vertical wind shear parameter, ocean thermal energy, (saturated) equivalent potential temperature gradient, and middle-troposphere humidity] were analyzed as potential predictors of tropical cyclogenesis for the Australian–southwest Pacific Ocean region. Various predictor lead times of up to 5 months were investigated, with the most significant Poisson regression models being cross validated, and the skill of their hindcasts evaluated. The Poisson regression model incorporating a combination of saturated equivalent potential temperature gradients at various leads was found to be the most skillful in hindcasting the temporal (phase and amplitude) variability of tropical cyclogenesis for the Australian–southwest Pacific region, with a correlation coefficient between the observed and cross-validated hindcast time series of 0.54 (significant at the 99% level), and a root- mean-square error 26% better than climatology. Models using the thermal (ocean thermal energy, saturated equivalent potential temperature gradient, and middle-troposphere humidity) and all (thermal plus low-level relative vorticity and vertical wind shear parameter) predictor variables showed the most skill in hindcasting the spatial distribution of cyclogenesis in this region. The model hindcast skill in predicting individual tropical cyclone occurrences and nonoccurrences was also examined. The all-Gray parameter Poisson regression model was found to correctly hindcast up to 72.6% of cyclogenesis events and nearly 70% of nonoccurrences in the Australian—southwest Pacific region. The model design enabled the investigation of tropical cyclogenesis on subregional/subseasonal scales, with promising model hindcast skill evident. The results presented herein suggest that useful and more detailed forecasts may be possible in the future in addition to those currently provided at the basin-wide and seasonal scales. [ABSTRACT FROM AUTHOR]

The impact of assimilating dropsonde data from the 2020 Atmospheric River (AR) Reconnaissance (ARR) field campaign on operational numerical weather forecasts was assessed. Two experiments were executed for the period from 24 January to 18 March 2020 using the National Centers for Environmental Prediction (NCEP) Global Forecast System version 15 (GFSv15) with a four-dimensional hybrid ensemble-variational (4DEnVar) data assimilation system. The control run (CTRL) used all of the routinely assimilated data and included data from 628 ARR dropsondes, whereas the denial run (DENY) excluded the dropsonde data. Results from 17 intensive observing periods (IOPs) indicate a mixed impact for mean sea level pressure and geopotential height over the Pacific-North American (PNA) region in CTRL compared to DENY. The overall local impact over the U.S. WestCoast and Gulf of Alaska for the 17 IOPs is neutral (20.45%) for integrated vapor transport (IVT), but positive for wind and moisture profiles (0.5%-1.0%), with a spectrum of statistically significant positive and negative impacts for various IOPs. The positive dropsonde data impact on precipitation forecasts over U.S. West Coast domains appears driven, in part, by improved low-level moisture and wind fields at short-forecast lead times. Indeed, data gaps, especially for accurate and unbiased moisture profiles and wind fields, can be at least partially mitigated to improve U.S. West Coast precipitation forecasts. [ABSTRACT FROM AUTHOR]

Florida annually leads the United States in lightning-caused fatalities. While many studies have examined the lightning frequency maximum near Cape Canaveral, relatively little attention has been paid to the western Florida peninsula, which features a similar warm-season lightning event density. Of particular concern are first cloud-to-ground (FCG) lightning events in developing thunderstorms, which are difficult to predict with sufficient lead time and can catch people off guard. This study performs an environmental analysis of warm-season (May–September) FCG events (2014–21) across the western Florida peninsula using high-resolution model analysis data, including a comparison to null (No CG) days. FCG events and No CG days are first identified from ground-based lightning data and partitioned into nine synoptic-scale flow regimes. Next, spatiotemporal distributions of FCG events are elucidated for the western Florida peninsula. An ingredients-based analysis shows that the convective environment one hour before FCG events during strong south-southeast flow features the largest amounts of moisture, but the smallest instability values and weak midtropospheric lapse rates, primarily due to warm advection and moisture transport from the Atlantic Ocean. Environments one hour before FCG events in all nine flow regimes feature markedly greater instability values, larger relative humidity values, and steeper midtropospheric lapse rates than do No CG days. Results emphasize that instability and moisture are the key ingredients for warm-season FCG events in the region. Convective parameter statistical distributions and composite soundings populate an online dashboard that can be used by regional forecasters to better predict FCG events and increase alert lead times. Significance Statement: Florida annually leads the United States in lightning fatalities. Of particular concern are first cloud-to-ground (FCG) lightning events, which are difficult to forecast and can catch people off guard especially during outdoor recreational activities and labor. We investigate the environmental characteristics of warm-season FCG events across the western Florida peninsula. Among nine regional flow patterns, some are associated with a less moist and more unstable atmosphere one hour before an FCG event, while other regimes exhibit a more moist and less unstable atmosphere. However, regardless of flow pattern, FCG events consistently feature substantially greater instability and moisture than do null events. Key findings are displayed on an online dashboard, to better inform regional forecasters. [ABSTRACT FROM AUTHOR]

Epidemic asthma events represent a significant risk to emergency services as well as the wider community. In southeastern Australia, these events occur in conjunction with relatively high amounts of grass pollen during the late spring and early summer, which may become concentrated in populated areas through atmospheric convergence caused by a number of physical mechanisms including thunderstorm outflow. Thunderstorm forecasts are therefore important for identifying epidemic asthma risk factors. However, the representation of thunderstorm environments using regional numerical weather prediction models, which are a key aspect of the construction of these forecasts, have not yet been systematically evaluated in the context of epidemic asthma events. Here, we evaluate diagnostics of thunderstorm environments from historical simulations of weather conditions in the vicinity of Melbourne, Australia, in relation to the identification of epidemic asthma cases based on hospital data from a set of controls. Skillful identification of epidemic asthma cases is achieved using a thunderstorm diagnostic that describes near-surface water vapor mixing ratio. This diagnostic is then used to gain insights on the variability of meteorological environments related to epidemic asthma in this region, including diurnal variations, long-term trends, and the relationship with large-scale climate drivers. Results suggest that there has been a long-term increase in days with high water vapor mixing ratio during the grass pollen season, with large-scale climate drivers having a limited influence on these conditions. Significance Statement: We investigate the atmospheric conditions associated with epidemic thunderstorm asthma events in Melbourne, Australia, using historical model simulations of the weather. Conditions appear to be associated with high atmospheric moisture content, which relates to environments favorable for severe thunderstorms, but also potentially pollen rupturing as suggested by previous studies. These conditions are shown to be just as important as the concentration of grass pollen for a set of epidemic thunderstorm asthma events in this region. This means that weather model simulations of thunderstorm conditions can be incorporated into the forecasting process for epidemic asthma in Melbourne, Australia. We also investigate long-term variability in atmospheric conditions associated with severe thunderstorms, including relationships with the large-scale climate and long-term trends. [ABSTRACT FROM AUTHOR]

To maximize the contribution of the Cross-track Infrared Sounder (CrIS) measurements to the global weather forecasting, we attempt to choose the CrIS channels to be assimilated in the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS). From preselected 431 CrIS channels, 207 channels are newly selected using a one-dimensional variational (1D-Var) approach where the channel score index (CSI) is used as a figure of merit. Newly selected 207 channels comprise 85 temperature, 49 water vapor, and 73 surface channels, respectively. In addition, to examine how the channels are selected if the forecast error covariance is differently defined depending on the latitudinal regions (i.e., Northern and Southern Hemispheres, and tropics), the same selection process is carried out repeatedly using three regional forecast error covariances. From three regional channel sets, two-channel sets are made for the global data assimilation. One channel set is made with 134 channels overlapped between three regional channel sets. Another channel set consists of 277 channels that is the sum of 3 regional channel sets. In the global trial experiments, the global CrIS 207 channels have a significant positive forecast impact in terms of the improvement of GFS global forecasting, as compared with the forecasts with the operational 100 channels as well as the overlapped 134 and the union 277 channel sets. The improved forecast is mainly due to the additional temperature/water vapor channels of the global CrIS 207 channels that are selected optimally based on the global forecast error of operational GFS. Significance Statement: The Cross-track Infrared Sounder (CrIS), with a total of 2211 channels, provides spectral measurements including unique information on the vertical structure of temperature and moisture as well as the atmospheric trace gases. However, assimilating all CrIS channels is practically difficult due to the huge computation cost and data volume. For this reason, we attempted to select the optimal CrIS channels via the physical selection method that takes into account the forecast/observation error characteristics in the numerical model where the CrIS measurements are practically assimilated. In the trial experiment, the newly selected CrIS channels have a positive forecast impact, highlighting that it is important to choose the channels physically by considering the relevant physical characteristics. [ABSTRACT FROM AUTHOR]

A 50-member convection-allowing ensemble is used to examine effects of daytime PBL evolution and ambient flow interacting with modest terrain features on convection initiation (CI) in the lee of the Rocky Mountains. The examined case (4 June 2015) has isolated supercell storms that initiate during mid- to late afternoon along the northern portion of the Palmer Lake Divide, which is a ~0.5-km-deep zonally oriented terrain feature in east-central Colorado that extends eastward from the Rocky Mountains. To diagnose factors most crucial to storm development, two 10-member subensembles are constructed from the full 50-member ensemble. One subensemble (STRONG) has storm locations with mature storm intensities, and average timing of CI similar to that observed. The other subensemble (WEAK) has fewer storms, with generally weaker intensity, and delayed CI. Environmental composites constructed from these subensembles reveal a stronger surface horizontal convergence zone and moisture gradient in STRONG, resulting from 2-3.5 m s-1 stronger southerly winds on the south flank of the convergence zone. The stronger southerlies result from accelerated PBL growth and momentum mixing in the presence of strong low-to-midtropospheric vertical shear, which is facilitated by reduced above-PBL static stability in the composite STRONG initial condition. Stronger time-averaged low-to-midtropospheric upward motion coincides with the surface convergence zone in STRONG, and individual CI locations occur at the northeastern edge of the composite vertical motion maximum. Trajectory analysis with STRONG members confirms that the CI locations are consistent with large vertical displacements, and corresponding relative humidity increases leading to decreases in convective inhibition, as the southerly airstream ascends across the convergence zone. [ABSTRACT FROM AUTHOR]

The tropical west Pacific Ocean and the Philippines are often affected by tropical cyclones (TCs), with threats to human life and of severe economic damage. The performance of the Met Office global operational forecasts at predicting TC-related precipitation is examined between 2006 and 2017, the first time total TC rainfall has been analyzed in a long-term forecast dataset. All precipitation falling within 5° of a TC track point is assumed to be part of the TC rainbands. Forecasts are verified against TC tracks from the JRA-55 reanalysis and precipitation from TRMM 3B42. In composites from the forecasts, the total precipitation (TC and non-TC) is too high and the TC-related precipitation is too low, over both ocean and the Philippines. These biases exist all year-round and generally worsen with lead time, but have improved in recent years with upgrades to the forecasting system. Biases in TC-related precipitation in the Philippines are attributable mainly to TC lifetime being too short over land and ocean and (over land) possibly to individual TCs producing too little rain. There are considerable biases in predicted large-scale conditions related to TC intensification, particularly too little lower-troposphere relative humidity and too strong vertical wind shear. The shear appears to have little impact on the amount of TC precipitation, but dry biases in humidity are consistent with dry biases in TC rainfall. The forecast system accurately reproduces the impact of the MJO on TC precipitation, relative to the forecasts' own climatology, potentially providing the opportunity for predictability out to several weeks. [ABSTRACT FROM AUTHOR]

Using the interior spectral nudging and update cycle (SN1UIC) methods in the regional Weather Research and Forecasting (WRF) Model, the numerical predictions of four persistent severe rainfall (PSR) events during the preflood season in south China were investigated, based on the fact that the global model has an advantage in predicting the large-scale atmospheric variation and the regional model is better in terms of simulating small-scale changes. The simulation results clearly indicated that the SN1UIC improved the prediction of the PSR events' daily precipitation for moderate, heavy, and torrential rains (10-100mmday-1). It also improved the simulative forecasts of the two categories of rain with accumulated precipitation above 50 and 100mm at lead times of 5-11 days. Moreover, the longer the forecast lead time is, the larger the decrease in the Brier score. Additionally, the SN1UIC method decreased the root-mean-square error for accumulated rainfall (6.2%) and relative humidity (5.67%). [ABSTRACT FROM AUTHOR]

Stable cold pools in California's Central Valley (CV) are conducive to freezing temperatures, high relative humidity, and, in some cases, fog. In this study it will be shown that the Weather Research and Forecasting (WRF) Model as commonly configured cannot reproduce such conditions because of a persistent warm and dry bias near the surface. It was found that removing horizontal diffusion, which by default operates on model levels and thus up and down the valley's sides, can reduce but not entirely fix the problem. Other improvements include enhancing the near-surface vertical resolution and the surface-air coupling, as both directly control the surface fluxes, especially evaporation. However, these alterations actually have the largest impact in the forested region surrounding the Central Valley, and influence the nighttime relative humidity in the CV only indirectly via nocturnal drainage flows. While it is not clear how realistic are the increased evaporation in the forest or the drainage flows, how and why these alterations result in significantly improved relative humidity reconstructions within the Central Valley are shown. [ABSTRACT FROM AUTHOR]

In a recent article, Qian et al. introduced the quantities moist vorticity and moist divergence to diagnose locations of heavy rain. These quantities are constructed by multiplying the relative vorticity and divergence by relative humidity to the power k, where k = 10 in their article. Their approach is similar to that for the previously constructed quantity generalized moist potential vorticity. This comment critiques the approach of Qian et al., demonstrating that the moist vorticity, moist divergence, and by extension generalized moist potential vorticity are flawed mathematically and meteorologically. Raising relative humidity to the 10th power is poorly justified and is based on a single case study at a single time. No meteorological evidence is presented for why areas of moist vorticity and moist divergence should overlap with regions of 24-h accumulated rainfall. All three quantities have not been verified against the output of precipitation directly from the model nor is the approach of combining meteorological quantities into a single parameter appropriate in an ingredients-based forecasting approach. Researchers and forecasters are advised to plot the model precipitation directly and employ an ingredients-based approach, rather than rely on these flawed quantities. [ABSTRACT FROM AUTHOR]

Properly including moisture effects into a dynamical parameter can significantly increase the parameter's ability to diagnose heavy rain locations. The relative humidity-based weighting approach used to extend the moist potential vorticity (MPV) to the generalized moist potential vorticity (GMPV) is analyzed and demonstrates such an improvement. Following the same approach, two new diagnostic parameters, moist vorticity (MV) and moist divergence (MD), have been proposed in this study by incorporating moisture effects into the traditional vorticity and divergence. A regional heavy rain event that occurred along the Yangtze River on 1 July 1991 is used as a case study, and 41 daily regional heavy rain events during the notorious flooding year of 1998 in eastern China are used for a systematic evaluation. Results show that after the moisture effects were properly incorporated, the improved ability of all three parameters to capture a heavy rain area is significant (statistically at the 99% confidence level): the GMPV is improved over the MPV by 194%, the MD over the divergence by 60%, and the MV over the vorticity by 34% in terms of the threat score (TS). The average TS is 0.270 for the MD, 0.262 for the MV, and 0.188 for the GMPV. Application of the MV and MD to assess heavy rain potential is not intended to replace a complete, multiscale forecasting methodology; however, the results from this study suggest that the MV and MD could be used to postprocess a model forecast to potentially improve heavy rain location predictions. [ABSTRACT FROM AUTHOR]

The Fengyun-3 series of satellites (FY-3) began in May 2008 with the launch of FY-3A. The onboard Microwave Humidity Sounders (MWHSs) provide vertical information about water vapor, which is important for numerical weather prediction (NWP). The noise equivalent delta temperature (NEDT) of the MWHS is higher than that of the Microwave Humidity Sounder (MHS) instrument (e.g., on board MetOp-B) but lower than that of the older AMSU-B instruments (on board NOAA-15, NOAA-16, and NOAA-17). Assimilation of MWHS observations into the ECMWF Integrated Forecast System (IFS) improved the fit of short-range forecasts to other observations, notably MHS, and also slightly improved the longer-range forecast scores verified against analyses. Also, assimilating the MWHS on board both FY-3A and FY-3B gave a larger impact than either instrument alone. Furthermore, when MWHS and MHS were added separately to a baseline using neither, the impact of MWHS was found to be comparable to that of MHS. Consequently, ECMWF has been assimilating the FY-3B MWHS data in the operational forecasting system since 24 September 2014. This is the first operational use of Chinese polar-orbiting satellite data by an NWP center outside of China. [ABSTRACT FROM AUTHOR]

Navy Operational Global Atmospheric Prediction System (NOGAPS) analysis and operational forecasts are evaluated against the Interim ECMWF Re-Analysis (ERA-Interim; ERAI) and satellite data, and compared with the Global Forecast System (GFS) analysis and forecasts, using both performance- and physics-based metrics. The NOGAPS analysis captures realistic Madden-Julian oscillation (MJO) signals in the dynamic fields and the low-level premoistening leading to active convection, but the MJO signals in the relative humidity (RH) and diabatic heating rate (Q1) fields are weaker than those in the ERAI or the GFS analysis. The NOGAPS forecasts, similar to the GFS forecasts, have relatively low prediction skill for the MJO when the MJO initiates over the Indian Ocean and when active convection is over the Maritime Continent. The NOGAPS short-term precipitation forecasts are broadly consistent with the Climate Prediction Center (CPC) morphing technique (CMORPH) precipitation results with regionally quantitative differences. Further evaluation of the precipitation and column water vapor (CWV) indicates that heavy precipitation develops too early in the NOGAPS forecasts in terms of the CWV, and the NOGAPS forecasts show a dry bias in the CWV increasing with forecast lead time. The NOGAPS underpredicts light and moderate-to-heavy precipitation but overpredicts extremely heavy rainfall. The vertical profiles of RH and Q1 reveal a dry bias within the marine boundary layer and a moist bias above. The shallow heating mode is found to be missing for CWV < 50 mm in the NOGAPS forecasts. The diabatic heating biases are associated with weaker trade winds, weaker Hadley and Walker circulations over the Pacific, and weaker cross-equatorial flow over the Indian Ocean in the NOGAPS forecasts. [ABSTRACT FROM AUTHOR]

An extended three-dimensional variational data assimilation (3DVAR) method based on the Weather Research and Forecasting Model (WRF) is developed to assimilate satellite-derived humidity from sea fog at its initial stage over the Yellow Sea. The sea fog properties, including its horizontal distribution and thickness, are retrieved empirically from the infrared and visible cloud imageries of the Multifunctional Transport Satellite (MTSAT). Assuming a relative humidity of 100% in fog, the MTSAT-derived humidity is assimilated by the extended 3DVAR assimilation method. Two sea fog cases, one spread widely over the Yellow Sea and the other spread narrowly along the coast, are first studied in detail with a suite of experiments. For the widespread-fog case, the assimilation of MTSAT-derived information significantly improves the forecast of the sea fog area, increasing the probability of detection and equitable threat scores by about 20% and 15%, respectively. The improvement is attributed to a more realistic representation of the marine boundary layer (MBL) and better descriptions of moisture and temperature profiles. For the narrowly spread coastal case, the model completely fails to reproduce the sea fog event without the assimilation of MTSAT-derived humidity. The extended 3DVAR assimilation method is then applied to 10 more sea fog cases to further evaluate its effect on the model simulations. The results reveal that the assimilation of MTSAT-derived humidity not only improves sea fog forecasts but also provides better moisture and temperature structure information in the MBL. [ABSTRACT FROM AUTHOR]

This study evaluates 24-h forecasts of dryline position from an experimental 4-km grid-spacing version of the Weather Research and Forecasting Model (WRF) run daily at the National Severe Storms Laboratory (NSSL), as well as the 12-km grid-spacing North America Mesoscale Model (NAM) run operationally by the Environmental Modeling Center of NCEP. For both models, 0000 UTC initializations are examined, and for verification 0000 UTC Rapid Update Cycle (RUC) analyses are used. For the period 1 April-30 June 2007-11, 116 cases containing drylines in all three datasets were identified using a manual procedure that considered specific humidity gradient magnitude, temperature, and 10-m wind. For the 24-h NAM forecasts, no systematic east-west dryline placement errors were found, and the majority of the east-west errors fell within the range ±0.5° longitude. The lack of a systematic bias was generally present across all subgroups of cases categorized according to month, weather pattern, and year. In contrast, a systematic eastward bias was found in 24-h NSSL-WRF forecasts, which was consistent across all subgroups of cases. The eastward biases seemed to be largest for the subgroups that favored 'active' drylines (i.e., those associated with a progressive synoptic-scale weather system) as opposed to 'quiescent' drylines that tend to be present with weaker tropospheric flow and have eastward movement dominated by vertical mixing processes in the boundary layer. [ABSTRACT FROM AUTHOR]

This study presents a spatial and temporal climatology of major wildfire events, defined as >100 acres burned (>40.47 ha, where 1 ha = 2.47 acre), in the northeast United States from 1999 to 2009 and the meteorological conditions associated with these events. The northeast United States is divided into two regions: region 1 is centered over the higher terrain of the northeast United States and region 2 is primarily over the coastal plain. About 59% of all wildfire events in these two regions occur in April and May, with ~76% in region 1 and ~53% in region 2. There is large interannual variability in wildfire frequency, with some years having 4-5 times more fire events than other years. The synoptic flow patterns associated with northeast United States wildfires are classified using the North American Regional Reanalysis. The most common synoptic pattern for region 1 is a surface high pressure system centered over the northern Appalachians, which occurred in approximately 46% of all events. For region 2, the prehigh anticyclone type extending from southeast Canada and the Great Lakes to the northeast United States is the most common pattern, occurring in about 46% of all events. A trajectory analysis highlights the influence of large-scale subsidence and decreasing relative humidity during the events, with the prehigh pattern showing the strongest subsidence and downslope drying in the lee of the Appalachians. [ABSTRACT FROM AUTHOR]

Northwest flow snowfall (NWFS) impacts the southern Appalachian Mountains after the upper-level trough has departed from the region, when moist northwesterly flow near the ground is lifted after encountering the mountains. Snowfall associated with these events is highly localized and challenging to predict as the clouds generating the accumulation are mesoscale in structure and depend on rapidly varying structures of moisture, instability, and wind in the planetary boundary layer (PBL) and on the relief of the local topography. The purpose of this study is to investigate the near-term impact of heat and moisture fluxes at the ground on the evolution of an NWFS event using several simulations of the Advanced Research core of the Weather Research and Forecasting mesoscale model. Model simulations indicate that convective banding is responsible for the snowfall accumulations in the southern Appalachians during the event and the structure of the banding is sensitive to the vertical positions of maximum wind shear and minimal stability within the PBL. Sensible heat fluxes at the ground upstream of the mountains in the daytime tend to deepen the PBL, reduce cloud water content, and reduce snowfall accumulations. At the same time, however, the daytime sensible heating also increases the overall vapor of the PBL through increased turbulent mixing and the transport of vapor made available to the atmosphere through upward latent heat fluxes at the ground. Latent heat fluxes at the ground upstream of the southern Appalachian Mountains provide a source of moisture that contributes a significant fraction of the overall simulated snowfall accumulations. [ABSTRACT FROM AUTHOR]

Advanced infrared (IR) sounders such as the Atmospheric Infrared Sounder (AIRS) and Infrared Atmospheric Sounding Interferometer (IASI) provide atmospheric temperature and moisture profiles with high vertical resolution and high accuracy in preconvection environments. The derived atmospheric stability indices such as convective available potential energy (CAPE) and lifted index (LI) from advanced IR soundings can provide critical information 1 ~ 6 h before the development of severe convective storms. Three convective storms are selected for the evaluation of applying AIRS full spatial resolution soundings and the derived products on providing warning information in the preconvection environments. In the first case, the AIRS full spatial resolution soundings revealed local extremely high atmospheric instability 3 h ahead of the convection on the leading edge of a frontal system, while the second case demonstrates that the extremely high atmospheric instability is associated with the local development of severe thunderstorm in the following hours. The third case is a local severe storm that occurred on 7-8 August 2010 in Zhou Qu, China, which caused more than 1400 deaths and left another 300 or more people missing. The AIRS full spatial resolution LI product shows the atmospheric instability 3.5 h before the storm genesis. The CAPE and LI from AIRS full spatial resolution and operational AIRS/AMSU soundings along with Geostationary Operational Environmental Satellite (GOES) Sounder derived product image (DPI) products were analyzed and compared. Case studies show that full spatial resolution AIRS retrievals provide more useful warning information in the preconvection environments for determining favorable locations for convective initiation (CI) than do the coarser spatial resolution operational soundings and lower spectral resolution GOES Sounder retrievals. [ABSTRACT FROM AUTHOR]

In 2006, the National Centers for Environmental Prediction (NCEP) implemented the Real-Time Mesoscale Analysis (RTMA) in collaboration with the Earth System Research Laboratory and the National Environmental, Satellite, and Data Information Service (NESDIS). In this work, a description of the RTMA applied to the 5-km resolution conterminous U.S. grid of the National Digital Forecast Database is given. Its two-dimensional variational data assimilation (2DVAR) component used to analyze near-surface observations is described in detail, and a brief discussion of the remapping of the NCEP stage II quantitative precipitation amount and NESDIS Geostationary Operational Environmental Satellite (GOES) sounder effective cloud amount to the 5-km grid is offered. Terrain-following background error covariances are used with the 2DVAR approach, which produces gridded fields of 2-m temperature, 2-m specific humidity, 2-m dewpoint, 10-m U and V wind components, and surface pressure. The estimate of the analysis uncertainty via the Lanczos method is briefly described. The strength of the 2DVAR is illustrated by (i) its ability to analyze a June 2007 cold temperature pool over the Washington, D.C., area; (ii) its fairly good analysis of a December 2008 mid-Atlantic region high-wind event that started from a very weak first guess; and (iii) its successful recovery of the finescale moisture features in a January 2010 case study over southern California. According to a cross-validation analysis for a 15-day period during November 2009, root-mean-square error improvements over the first guess range from 16%% for wind speed to 45%% for specific humidity. [ABSTRACT FROM AUTHOR]

The high vertical density soundings recorded during the 2006 African Monsoon Multidisciplinary Analysis (AMMA) campaign are assimilated into the French numerical weather prediction Action de Recherche Petite Echelle Grande Echelle (ARPEGE) four-dimensional variational data assimilation (4DVAR) system, with and without a bias correction for relative humidity. Four different experiments are carried out to assess the impacts of the added observations. The analyses and forecasts from these different scenarios are evaluated over western Africa. For the full experiment using all data together with a bias correction, the humidity analysis is in better agreement with surface observations and independent GPS observations than it was for the other experiments. AMMA data also improve the African easterly jet (AEJ) on its southeasterly side, and when they are used with an appropriate bias correction, the daily and monthly averaged precipitation results are in relatively good agreement with the satellite-based precipitation estimates. Forecast scores are computed with respect to surface observations, radiosondes, and analyses from the European Centre for Medium-Range Weather Forecasts (ECMWF). The positive impacts of additional radiosonde observations (with a relevant bias correction) are found to propagate downstream with a positive impact over Europe at the 2–3-day forecast range. [ABSTRACT FROM AUTHOR]

This study identifies and examines the common characteristics of several nocturnal midlatitude mesoscale convective systems (MCSs) that developed near mesoscale convective vortices (MCVs) or cutoff lows. All of these MCSs were organized into convective clusters or lines that exhibited back-building behavior, remained nearly stationary for 6–12 h, and produced locally excessive rainfall (greater than 200 mm in 12 h) that led to substantial flash flooding. Examination of individual events and composite analysis reveals that the MCSs formed in thermodynamic environments characterized by very high relative humidity at low levels, moderate convective available potential energy (CAPE), and very little convective inhibition (CIN). In each case, the presence of a strong low-level jet (LLJ) and weak midlevel winds led to a pronounced reversal of the wind shear vector with height. Most of the MCSs formed without any front or preexisting surface boundary in the vicinity, though weak boundaries were apparent in two of the cases. Lifting and destabilization associated with the interaction between the LLJ and the midlevel circulation assisted in initiating and maintaining the slow-moving MCSs. Based on the cases analyzed in this study and past events described in the literature, a conceptual model of the important processes that lead to extreme rainfall near midlevel circulations is presented. [ABSTRACT FROM AUTHOR]

Antarctic Mesoscale Prediction System (AMPS) forecasts of atmospheric moisture and cloud fraction (CF) are compared with observations at McMurdo and Amundsen–Scott South Pole station (hereafter, South Pole station) in Antarctica. Overall, it is found that the model produces excessive moisture at both sites in the mid- to upper troposphere because of a weaker vertical decrease of moisture in AMPS than observed. Correlations with observations suggest AMPS does a reasonable job of capturing the low-level moisture variability at McMurdo and the upper-level moisture variability at South Pole station. The model underpredicts the cloud cover at both locations, but changes to the AMPS empirical CF algorithm remove this negative bias by more than doubling the weight given to the cloud ice path. A “pseudosatellite” product based on the microphysical quantities of cloud ice and cloud liquid water within AMPS is preliminarily evaluated against Defense Meteorological Satellite Program (DMSP) imagery during summer to examine the broader performance of cloud variability in AMPS. These comparisons reveal that the model predicts high-level cloud cover and movement with fidelity, which explains the good agreement between the modified CF algorithm and the observed CF. However, this product also demonstrates deficiencies in capturing low-level cloudiness over cold ice surfaces primarily related to insufficient supercooled liquid water produced by the microphysics scheme, which also reduces the CF correlation with observations. The results suggest that AMPS predicts the overall CF amount and high cloud variability notably well, making it a reliable tool for longer-term climate studies of these fields in Antarctica. [ABSTRACT FROM AUTHOR]

Meteorological forcing data are necessary to drive many of the spatial models used to simulate atmospheric, biological, and hydrological processes. Unfortunately, many domains lack sufficient meteorological data and available point observations are not always suitable or reliable for landscape or regional applications. NOAA’s Local Analysis and Prediction System (LAPS) is a meteorological assimilation tool that employs available observations (meteorological networks, radar, satellite, soundings, and aircraft) to generate a spatially distributed, three-dimensional representation of atmospheric features and processes. As with any diagnostic representation, it is important to ascertain how LAPS outputs deviate from a variety of independent observations. A number of surface observations exist that are not used in the LAPS system, and they were employed to assess LAPS surface state variable and precipitation analysis performance during two consecutive years (1 September 2001–31 August 2003). LAPS assimilations accurately depicted temperature and relative humidity values. The ability of LAPS to represent wind speed was satisfactory overall, but accuracy declined with increasing elevation. Last, precipitation estimates performed by LAPS were irregular and reflected inherent difficulties in measuring and estimating precipitation. [ABSTRACT FROM AUTHOR]

Mobile mesonet line normal, time-to-space converted data analysis on three meridional drylines that occurred in west Texas on 10 June 1999 and 5 May 2000 are presented herein; two occurred in a quiescent environment on 5 May 2000. Based on the data, the mixing zone across the dryline was composed of a series of large horizontal moisture differentials that were highly variable in width, ranging from 5 km down to several hundred meters. The largest dewpoint differential sampled was 10.0°C over 185 m. Concurrent with a deceleration of dryline movement to nearly stationary, and while moisture differentials strengthened, surface-based mesoscale vertical circulations with horizontal diameters of 2 km down to less than 300 m were resolved in the data, and visual observations were made of numerous, strongly rotating dust devils. The estimated diameters of the largest dust devils were ∼80–100 m and ∼1 km deep, and these persisted for tens of minutes. All vortices were found to move along or adjacent to the zones of moisture differential. Additionally, when the circulations were observed, spatially isolated cumulus clouds located along the dryline exhibited rapid vertical development. It is plausible that the vortices protect an ascending air parcel by inhibiting mixing, thus allowing the parcel to reach its local lifting condensation level and level of free convection with relatively greater buoyancy than parcels not contained in vortices. [ABSTRACT FROM AUTHOR]

The skill of a mesoscale-model-based model output statistics (MOS) system that provided hourly forecasts for 18 sites over northern Utah during the 2002 Winter Olympic and Paralympic Games is evaluated. The MOS system was developed using three winters (November–April 1998/99, 1999/2000, and 2000/01) of forecasts by the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) and observations from Olympic venues and transportation corridors. MOS temperature, relative humidity, wind speed, and wind direction forecasts were considerably more accurate than those produced by the 12- and 4-km MM5 grids. A primary contributor to MM5 temperature and relative humidity errors was a systematic overprediction of surface temperature (i.e., a warm/dry bias) during persistent and nocturnal cold-pool events when corresponding errors in MM5 dewpoint temperature forecasts were not observed. MOS largely corrected for this temperature bias. MOS wind speed forecasts outperformed the 12- and 4-km MM5 forecasts by the largest margin at locations with the lowest wind speed variability. Raw model and MOS performance exhibited minimal sensitivity to variations in model initial and lateral boundary conditions (derived from the forecasts of either the National Centers for Environmental Prediction's Eta Model or the Aviation run of the Global Spectral Model). MOS temperature, relative humidity, and wind speed forecasts were equal to or more skillful than human-generated forecasts produced by the Olympic Forecast Team. The results illustrate that statistical techniques continue to improve upon purely numerical predictions even at high resolution. This is particularly true in a region of complex terrain where detailed characteristics of local topography and microclimates remain unresolved. It is recommended that traditional MOS or other statistical techniques based on high-density surface observations available from the MesoWest cooperative networks be used to improve gridded forecast products created by the National Weather Service Interactive Forecast Preparation System (IFPS) and other applications. [ABSTRACT FROM AUTHOR]

Evidence is presented to support the hypothesis that mesoscale convective complexes (MCCs) near the Rocky Mountains are more likely to form when the middle-tropospheric relative humidity is greater than average and the lower-tropospheric relative humidity is less than average. Radiosonde data for MCC events are chosen at the nearest place to first storm development and at the nearest time before first storms occurred. A sounding representing an average seasonally adjusted climatological location of orogenic MCC first storms was used to represent non-MCC days. The 500-hPa relative humidities were significantly higher for MCC events than for non-MCC days. The 700-hPa relative humidity was significantly lower for MCC events than for non-MCC days. MCC days also have somewhat less stability than non-MCC days but this factor appears to be related to higher temperatures at 500 hPa on days when the 500-hPa relative humidity is low. The values of various quantities used to assess the utility of this information for weather forecasting indicate that this method needs to be combined with other MCC forecasting methods to be useful. [ABSTRACT FROM AUTHOR]