Vertical Moisture Profiles Research Articles

Water Vapor Lidar Observation and Data Assimilation for a Moist Low-Level Jet Triggering a Mesoscale Convective System

Abstract We conducted field observations using two water vapor Raman lidars (RLs) in Kyushu, Japan, to clarify the characteristics of a moist low-level jet (MLLJ), which plays a fundamental role in the formation and maintenance of mesoscale convective systems (MCSs). The two RLs observed the inside and outside of an MLLJ, providing moisture to an MCS with local heavy precipitation on 9 July 2021. Our observations revealed that the MLLJ contained large amounts of moisture below the convective mixing layer height of 1.6 km. The large amount of moisture in the MLLJ might be intensified by low-level convergences and/or water vapor buoyancy facilitated by strong horizontal wind. We conducted four data assimilation experiments: CNTL that assimilated Japan Meteorological Agency operational observation data and three other experiments that ingested the lidar-derived vertical moisture profiles as well as the operational observation data. The experiments assimilating lidar-derived vertical moisture profiles caused intensification and southwestward extensions of the low-level convergence zone, resulting in local heavy precipitation at lower latitudes in experiments assimilating lidar-derived moisture profiles than in CNTL. All three experiments ingesting vertical moisture profiles generally produced better 9-h precipitation forecasts than CNTL, implying that the assimilation of vertical moisture profiles could be well suited for numerical weather prediction of local heavy precipitation. Moreover, the experiment assimilating both of the two RL sites’ data reproduced better forecast fields than experiments assimilating a single RL site’s data, implying that data assimilation of vertical moisture profiles at multiple RL sites enables us to improve initial conditions compared to a single RL site. Significance Statement Moist low-level jets (MLLJs) are moisture-rich airflows in the low-level atmosphere that play an important role in developing mesoscale convective systems and local heavy rainfall. To better understand the mechanisms affecting the development of local heavy rainfall events and to improve our ability to forecast them, studying the moisture structures in MLLJs is important. We succeeded in observing an MLLJ in western Japan using water vapor Raman lidars (RLs), which obtained vertical moisture profiles, and revealed details of vertical moisture structures in the MLLJ. We also performed data assimilation experiments to examine the impact of assimilating vertical moisture profiles observed by the RLs. The results showed that the assimilation of the moisture data improved the forecasting of local heavy rainfall.

Exploring the Use of Machine Learning to Improve Vertical Profiles of Temperature and Moisture

Abstract Vertical profiles of temperature and dewpoint are useful in predicting deep convection that leads to severe weather, which threatens property and lives. Currently, forecasters rely on observations from radiosonde launches and numerical weather prediction (NWP) models. Radiosonde observations are, however, temporally and spatially sparse, and NWP models contain inherent errors that influence short-term predictions of high impact events. This work explores using machine learning (ML) to postprocess NWP model forecasts, combining them with satellite data to improve vertical profiles of temperature and dewpoint. We focus on different ML architectures, loss functions, and input features to optimize predictions. Because we are predicting vertical profiles at 256 levels in the atmosphere, this work provides a unique perspective at using ML for 1D tasks. Compared to baseline profiles from the Rapid Refresh (RAP), ML predictions offer the largest improvement for dewpoint, particularly in the middle and upper atmosphere. Temperature improvements are modest, but CAPE values are improved by up to 40%. Feature importance analyses indicate that the ML models are primarily improving incoming RAP biases. While additional model and satellite data offer some improvement to the predictions, architecture choice is more important than feature selection in fine-tuning the results. Our proposed deep residual U-Net performs the best by leveraging spatial context from the input RAP profiles; however, the results are remarkably robust across model architecture. Further, uncertainty estimates for every level are well calibrated and can provide useful information to forecasters.

A preliminary development of a coupled surface and subsurface flow model for swale system

This study presents the preliminary stage of the development of a conjunctive surface-subsurface model for simulating the flow in a swale. The surface flow is modelled by one-dimensional dynamic wave equation. To improve accuracy, a third-order numerical scheme is used to solve the advection terms in the dynamic wave model. Meanwhile, the subsurface flow is modelled as one-dimensional vertical flow through nondeformable porous media without air compression effect. At preliminary stage, the surface and subsurface model are verified separately. The surface model is verified against the experimental data of surface runoff from a simulated rainfall. The surface model showed promising performance in terms of the reproduction of surface hydrograph. The overall discrepancy between the experimental and numerical model result in the reproduction of the hydrograph discharge is around 14%. For the subsurface flow model, the vertical moisture profile of the soil is verified against Philip’s analytical solution. The moisture profile obtained from the numerical model shows 100% agreement with Philip’s solution.

Зависимость отражательных свойств агропочв в сверхширокой полосе частот от типа, степени шероховатости поверхности и профилей влажности агропочв

In this article, the impact of volumetric moisture from 0% to 40% (vertical moisture profiles), dry bulk density from 0.4 to 1.8 g/cm3, clay content from 0.15 to 0.55 g/g (agrosoil type), standard deviations heights of surface roughness from 0 to 4 cm of soil on the variations of reflection coefficient in the ultra-wide frequency band from 100-400 MHz to 1.26-2.4 GHz were investigated. The reflection coefficient was calculated for a smooth and rough soil surface. Mironov's two-relaxation dielectric model was used (input parameters: dry bulk density, clay content, volumetric moisture, frequency of electromagnetic wave) to calculating the reflection coefficient. It is shown that the reflection coefficient is an ambiguous function of the clay content and the dry bulk density; the error in retrieving high values of soil moisture can be 5 times higher than for dry soils. In relation to the clay content and the dry bulk density, the soil roughness is the dominant parameter, which uncertainty, significantly effects on the error of soil moisture retrieving. For the considered various vertical profiles of soil moisture, it is shown that the effective thickness of topsoil, which forms the reflection coefficient, does not exceed 2 cm at a sensing frequency above 1 GHz. When the volumetric moisture content of soil surface is more than 28%, the reflection coefficient in the frequency range from 433 MHz to 1.26 GHz does not depend on the vertical distribution of moisture in soil. The conducted studies establish quantitative limitations on the accuracy of soil moisture retrieval for a given error in the initial parameters of the model: the clay content, the dry bulk density, soil surface roughness, which should be taken into account when developing algorithms for remote sensing of agrosoil moisture.

Dynamic Downscaling Simulation and Projection of Precipitation Extremes Over China Under a Shared Socioeconomic Pathway Scenario

AbstractIn this study, Weather Research and Forecasting (WRF) ‐based dynamic downscaling simulation was performed over China at a horizontal resolution of 25 km. To reduce the systematic large‐scale biases from lateral boundary conditions, a dynamic blending (DB) technique was introduced in downscaling, and its performance was compared with downscaling without DB (NO_DB) as well as the driving global climate model (GCM, i.e., HadGEM3). In the present‐day simulation for verification, added values were found in DB, relative to the GCM and NO_DB, in simulating the precipitation extremes, especially over southeastern China. Possible causes responsible for this improvement were further analyzed. In the GCM, excessive moisture and atmospheric heating in the upper troposphere in conjunction with abnormally strong deep convection resulted in excessive extreme precipitation over southeastern China, while in NO_DB, insufficient moisture and atmospheric heating in the whole troposphere in conjunction with abnormally weak convection suppressed extreme precipitation there. In comparison, DB showed a closer representation of observations in terms of vertical velocity and vertical profiles of atmospheric moisture and heating, accounting for the improved simulation of extreme precipitation. In the future projection under the SSP5‐8.5 scenario, both the GCM and DB predicted that extreme precipitation would increase in most parts of China; however, DB indicated a larger increase over southeastern China relative to the GCM. Stronger moisture flux convergence over southeastern China in DB accounted for this larger increase, and in addition, the thermodynamic effect associated with more precipitable water dominated the stronger moisture flux convergence.

Impact of Water Vapor on the Development of a Supercell Over Eastern China

In this study, the impact of water vapor on the improvement of precipitation simulation in a supercell event over eastern China is investigated by assimilating lightning data into the weather research and forecast (WRF) three-dimensional variational system. The results indicate that the vertical distribution of vapor plays a crucial role in convection precipitation simulation. The WRF alone fails to capture the vertical distribution of vapor, especially at higher altitudes, and vapor simulation is often weak in WRF. Assimilation of lightning data enhances the range and intensity of precipitation forecasts; assimilation of pseudo water vapor transformed from lightning data improves the convection intensification and updraft height of supercells by rectifying the vertical moisture profile. Lightning data assimilation can efficiently compensate for the paucity of observations during supercell growth, and the benefits of lightning data assimilation are concentrated in areas where lightning data is accessible. Further, the importance of microscale water vapor variations, which can be well represented by assimilating pseudo-water vapor, in representing the supercell’s range and intensity is highlighted.

How the Interaction of Heatwaves and Urban Heat Islands Amplify Urban Warming

An increase in global temperature will likely result in more intense and frequent heatwaves that would last longer. Simultaneously, the growth of urban population requires more areas of land incorporated into urbanization, because most people are expected to live in cities, which will increase the intensity and duration of urban heat islands. However, the extent of the link between global warming induced heatwaves and urbanization caused heat islands is barely understood. Understanding the link would give a new information about catastrophic heat mitigation strategies. This paper, therefore, quantifies, at the sub-continental scale of Eastern North America, the effects of background perturbations by the synergies between heatwaves and urban heat islands using simulations from the Weather Research and Forecasting (WRF) model, and focusing on the responses of urban energy balances, boundary layer height and vertical profiles of heat, momentum and moisture. Results showed that urban heat islands exacerbate heatwaves by deepening the turbulent boundary layer height, modifying the urban surface energy and regional winds. The fractional energy shift from latent to sensible heat fluxes and the consequent changes to the urban planetary boundary layer tends to amplify the intensity, extent and duration of extensive heatwaves. The response of ground heat fluxes to urban surfaces lags, while urban canopy humidity dissipates earlier because at the onset of the heatwave the surface water evaporates quickly to the point where there is less water left for evaporation leaving the urbanized regions vulnerable to more heating. During the heatwave event, the mean wind speed dropped by 2.5 m/s, hence less cool air is available for ventilation. The planetary boundary layer deepens by a maximum of 90-m over urban compared to rural and this may prolong urban surface heating. Based on the results, it can be concluded that the best heat-stress management strategies from the perspectives of urban energy balance and planetary boundary layer height is an integral approach that would lower sensible heat fluxes and increase surface albedo, latent heat fluxes and wind flows towards urban centers.

Influence of Dust on Precipitation During Landfalling Atmospheric Rivers in an Idealized Framework

AbstractAtmospheric rivers (ARs) can provide as much as 50% of the total annual rainfall to the United States West Coast via orographic precipitation. Dust is thought to enhance orographic precipitation via the “seeder‐feeder” mechanism, in which ice particles from a high cloud fall through a lower orographic cloud, seeding precipitation in the low cloud. Using the Weather Research and Forecasting model, we vary dust concentrations in simulations of two‐dimensional flow over a mountain. This idealized framework allows us to test the sensitivity of the precipitation‐dust response to a variety of different dust concentrations and initial conditions. The model is run using an ensemble of 60 radiosondes collected from Bodega Bay, CA in 2017–2018, clustered based on their vertical moisture profile into “deep moist,” “shallow moist,” and “subsaturated” clusters. The principle impact on precipitation is to increase the ratio of precipitation falling as snow. This produces a “spillover” effect, decreasing precipitation upwind of the peak and increasing precipitation downwind of the peak. The largest impacts on the snow/rain ratio occur at the end of the event, during cold front passage. The ensemble mean does not produce a significant seeder‐feeder response, however, in individual cases with favorable initial conditions there is a significant increase in precipitation throughout the domain due to dust effects on the seeder‐feeder mechanism. These findings afford an opportunity to build a more comprehensive understanding for the conditions under which dust aerosol can have a significant impact on precipitation during ARs, with implications for future developments in forecasting.

Observation of the Tropical Cyclone Diurnal Cycle Using Hyperspectral Infrared Satellite Sounding Retrievals

Abstract Hyperspectral infrared satellite sounding retrievals are used to examine thermodynamic changes in the tropical cyclone (TC) environment associated with the diurnal cycle of radiation. Vertical profiles of temperature and moisture are retrieved from the Suomi National Polar-Orbiting Partnership ( SNPP ) satellite system, National Oceanic and Atmospheric Administration-20 ( NOAA-20 ), and the Meteorological Operational ( MetOp-A/B ) satellite system, leveraging both infrared and microwave sounding technologies. Vertical profiles are binned radially based on distance from the storm center and composited at 4-h intervals to reveal the evolution of the diurnal cycle. For the three cases examined—Hurricane Dorian (2019), Hurricane Florence (2018), and Hurricane Irma (2017)—a marked diurnal signal is evident that extends through a deep layer of the troposphere. Statistically significant differences at the 95% level are observed in temperature, moisture, and lapse rate profiles, indicating a moistening and destabilization of the mid- to upper troposphere that is more pronounced near the inner core of the TC at night. Observations support a favorable environment for the formation of deep convection caused by diurnal differences in radiative heating tendencies, which could partially explain why new diurnal pulses tend to form around sunset. These findings demonstrate that the diurnal cycle of radiation affects TC thermodynamics through a deep layer of the troposphere, and suggest that hyperspectral infrared satellite sounding retrievals are valuable assets in detecting thermodynamic variations in TCs.

The Birmingham, Alabama Snow “Disaster” of 28 January 2014

A rare combination of anomalously cold air and rapidly falling snow produced a massive travel disruption in parts of the southeastern U.S. on 28 January 2014. Storm-total snowfalls were generally only 5 cm in the Birmingham, AL area, but most people were at work or school when the snow began. Due to icy roadways, thousands of people were forced to sleep in offices and schools, while many vehicles were abandoned on freeways and surface streets.
 This event was a unique forecasting challenge in many aspects, especially for the southeastern U.S. In the 24 h leading up to the event, numerical model quantitative precipitation forecasting (QPF) was focused south of the main population centers where the main disruptions eventually occurred. A layer of very dry air was present up to 800 hPa, but saturation deficits were low due to the cold air. Radar data a few hours before the event provided some of the only tangible evidence that significant snow may occur. However, forecasters did not anticipate the havoc that would ensue from the relatively light snow accumulations.
 In this paper, we examine the event, including the synoptic setup, vertical profiles of temperature and moisture, soil temperatures, numerical models, and radar data. On the early morning of 28 January, when school superintendents and business owners had to make decisions on whether to open, no warnings nor advisories were in effect for the majority of the Birmingham area, and the media indicated there would be no travel problems in Birmingham.

Toward Parametrization of Precipitating Shallow Cumulus Cloud Organization via Moisture Variance

AbstractThe influence of the initial vertical moisture profile on precipitating shallow cumulus cloud organization in terms of the column‐averaged moisture variance is investigated using large‐eddy simulations. Five idealized simulations based on the Rain in Cumulus over the Ocean field experiment with different initial moisture profiles are investigated. All cases simulate precipitating shallow cumulus convection in a marine sub‐tropical region under large‐scale subsidence. The results show that the moisture variance is mainly generated through the interaction of the moisture flux and the moisture gradient in the gradient production term at the top of the boundary layer. The development is characterized by three regimes: initial, transition, and quasi‐steady regime. During the initial regime, the moisture gradient is built up by moisture accumulation until precipitating convection starts. Within the transition regime, precipitation enables mesoscale cloud organization with enhanced convective activity and moisture fluxes. The moisture variance increases from the moist to the dry initial moisture profiles. In a following quasi‐steady regime, the moisture variance is approximately preserved. Thereby, the initial moisture gradient between the average sub‐cloud layer and the free atmosphere is found to be an important factor for the generation of the quasi‐steady column‐averaged moisture variance. The result suggests that a resolved‐scale variable like the moisture gradient can be used to estimate the quasi‐steady state conditions resulting from cloud organization. This finding may serve as a starting point for the parametrization of the subgrid scale cloud organization caused by precipitating shallow convection.

Evaluation of a land-atmosphere coupling metric computed from a ground-based infrared interferometer

AbstractLand-atmosphere feedbacks are a critical component of the hydrologic cycle. Vertical profiles of boundary layer temperature and moisture, together with information about the land surface, are used to compute land-atmosphere coupling metrics. Ground based remote sensing platforms, such as the Atmospheric Emitted Radiance Interferometer (AERI), can provide high temporal resolution vertical profiles of temperature and moisture. When co-located with soil moisture, surface flux, and surface meteorological observations, such as at the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site, it is possible to observe both the terrestrial and atmospheric legs of land-atmosphere feedbacks. In this study, we compare a commonly used coupling metric computed from radiosonde-based data to that obtained from the AERI to characterize the accuracy and uncertainty in the metric derived from the two distinct platforms. This approach demonstrates the AERI’s utility where radiosonde observations are absent in time and/or space. Radiosonde and AERI based observations of the Convective Triggering Potential and Low-Level Humidity Index (CTP-HIlow) were computed during the 1200 UTC observation time and displayed good agreement during both 2017 and 2019 warm seasons. Radiosonde and AERI derived metrics diagnosed the same atmospheric preconditioning based upon the CTP-HIlow framework a majority of the time. When retrieval uncertainty was considered, even greater agreement was found between radiosonde and AERI derived classification. The AERI’s ability to represent this coupling metric well enabled novel exploration of temporal variability within the overnight period in CTP and HIlow. Observations of CTP-HIlow computed within a few hours of 1200 UTC were essentially equivalent, however with greater differences in time arose greater differences in CTP and HIlow.

Climate Variability of Atmospheric Rivers and Droughts over the West Coast of the United States from 2006 to 2019

Water resources are crucial to the livelihood and sustainability of the general public across the western United States. This study covers the timespan of both the third driest drought in Californian history between 2012 and 2015 as well as the extreme atmospheric river year in 2016–2017. The evaluation of vertical moisture profiles using Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) Radio Occultation (RO) data, National Centers for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) Reanalysis of 500 hPa geopotential heights, 1000–500 hPa thickness, Optimum Interpolation (OI) Sea Surface Temperature (SST), NOAA/NDBC buoy data, and NASA, MEaSUREs, Gridded Sea Surface Height Anomalies (SSHA) were performed. The daily COSMIC time evolution from 2006 through 2015 showed a flat to slightly upward trend of both temperature and water vapor profiles through the entirety of the western US drought. Subsequently, a significant increase of temperatures and water vapor were recorded in early 2016 before the extreme Atmospheric River (AR) season of 2016–2017. The quantitative analyses suggest that warmer SST and higher SSHA lead to an increase of heat fluxes from the ocean into the troposphere, which forces thickness changes and thus the position of troughs in the geopotential height field changes afterwards, consequently pushing the trough eastward over the Pacific Northwest and potentially leading to an active AR year in the western US. It appears that regional COSMIC RO moisture profiles, seasonal SST, and SLH anomalies may serve as a precursor for seasonal or sub-seasonal precipitation outlook along the western US.

MIIDAPS-AI: An Explainable Machine-Learning Algorithm for Infrared and Microwave Remote Sensing and Data Assimilation Preprocessing - Application to LEO and GEO Sensors

In this article, we leverage and apply state-of-the-art artificial intelligence (AI) techniques to satellite remote sensing of temperature, moisture, surface, and cloud parameters in all-weather, all-surface conditions, from both microwave and infrared sensors. The multi-instrument inversion and data assimilation preprocessing system, artificial intelligence version, or MIIDAPS-AI for short, is valid for both polar and geostationary microwave and infrared sounders and imagers as well as for pairs of combined infrared and microwave sounders. The algorithm produces vertical profiles of temperature and moisture as well as surface temperature, surface emissivity, and cloud parameters. Additional products from hyperspectral infrared sensors include selected trace gases. From microwave sensors, additional products such as rainfall rate, first year/multiyear sea ice concentration, and soil moisture can be derived from primary products. The MIIDAPS-AI algorithm is highly efficient with no noticeable decrease in accuracy compared to traditional operational sounding algorithms. The automatically generated Jacobians from this deep-learning algorithm could provide an explainability mechanism to build trustworthiness in the algorithm, and to quantify uncertainties of the algorithm's outputs. The computation gain is estimated to be two orders of magnitude, which opens the door to either 1) process massively larger amounts of satellite data, or to 2) offer improvements in timeliness and significant saving in computing power (and therefore cost) if the same amount of data is processed. Here, we present an overview of the MIIDAPS-AI implementation, discuss its applicability to various sensors and provide an initial performance assessment for a select number of sensors and geophysical parameters.

The Effect of Climatological Variables on Future UAS-Based Atmospheric Profiling in the Lower Atmosphere

Vertical profiles of wind, temperature, and moisture are essential to capture the kinematic and thermodynamic structure of the atmospheric boundary layer (ABL). Our goal is to use weather observing unmanned aircraft systems (WxUAS) to perform the vertical profiles by taking measurements while ascending through the ABL and subsequently descending to the Earth’s surface. Before establishing routine profiles using a network of WxUAS stations, the climatologies of the flight locations must be studied. This was done using data from the North American Regional Reanalysis (NARR) model. To begin, NARR data accuracy was verified against radiosondes. While the results showed variability in individual profiles, the detailed statistical analyses of the aggregated data suggested that the NARR model is a viable option for the study. Based on these findings, we used NARR data to determine fractions of successful hypothetical flights of vertical profiles across the state of Oklahoma given thresholds of visibility, cloud base level (CBL) height, and wind speed. CBL height is an important parameter because the WxUAS must stay below clouds for the flight restrictions being considered. For the purpose of this study, a hypothetical WxUAS flight is considered successful if the vehicle is able to reach an altitude corresponding to a pressure level of 600 hPa. Our analysis indicated the CBL height parameter hindered the fractions of successful hypothetical flights the most and the wind speed tolerance limited the fractions of successful hypothetical flights most strongly in the winter months. Northwest Oklahoma had the highest fractions of successful hypothetical flights, and the southeastern corner performs the worst in every season except spring, when the northeastern corner performed the worst. Future work will study the potential effect of topology and additional variables, such as amount of rainfall and temperature, on fractions of successful hypothetical flights by region of the state.

Drainage of high-consistency fiber-laden aqueous foams

Lightweight lignocellulosic fibrous materials (LLFMs) offer a sustainable and biodegradable alternative in many applications. Enthusiastic interest in these materials has recently grown together with the newly risen interest in foam forming. Foam bubbles restrain fiber flocculation, and foam formed structures have high uniformity. Moreover, the bubbles support the fibrous structure during manufacturing enabling the formation of highly porous structures. Mechanical pressure cannot be applied in the manufacture of LLFMs as the materials would lose their porous structure. Water is therefore typically removed by a combination of drainage and thermal drying. Thermal drying of porous materials has been studied intensively. However, there are only a few studies on the drainage of fiber-laden foams. Thus, in this work, we conducted a systematic analysis of this topic. Our findings show that after drainage a stationary vertical moisture profile similar to that of pure foams is developed. Raising the initial fiber consistency was found to increase the final fiber consistency of the foam until the drainage ceased. Increasing mold height was found to increase the final consistency considerably. Without vacuum and heating, the shrinkage of samples during drainage was only slightly higher than the volume of the drained water. Drainage rate and final consistency increased clearly with increasing vacuum, but simultaneously sample shrinkage increased considerably. The best compromise was obtained with a vacuum of 0.5 kPa, which increased the final consistency by 60% without extra shrinkage. Using warm foam and heating the foam during drainage increased the final consistency considerably, but this also led to significant shrinkage of the sample.

Why Does Fog Deepen? An Analytical Perspective

The overall depth of a fog layer is one of the important factors in determining the hazard that a fog event presents. With discrete observations and often coarse numerical grids, however, fog depth cannot always be accurately determined. To address this, we derive a simple analytical relation that describes the change in depth of a fog interface with time, which depends on the tendencies and vertical gradients of moisture. We also present a lengthscale estimate for the maximum depth over which mixing can occur in order for the fog layer to be sustained, assuming a uniform mixing of the vertical profiles of temperature and moisture. Even over several hours, and when coarse observational resolution is used, the analytical description is shown to accurately diagnose the depth of a fog layer when compared against observational data and the results of large-eddy simulations. Such an analytical description not only enables the estimation of sub-grid or inter-observation fog depth, but also provides a simple framework for interpreting the evolution of a fog layer in time.

Simulations of Monsoon Intraseasonal Oscillation Using Climate Forecast System Version 2: Insight for Horizontal Resolution and Moist Processes Parameterization

In the present study, we analyze the Climate Forecast System version 2 (CFSv2) model in three resolutions, T62, T126, and T382. We evaluated the performance of all three resolutions of CFSv2 in simulating the Monsoon Intraseasonal Oscillation (MISO) of the Indian summer monsoon (ISM) by analyzing a suite of dynamic and thermodynamic parameters. Results reveal a slower northward propagation of MISO in all models with the characteristic northwest–southeast tilted rain band missing over India. The anomalous moisture convergence and vorticity were collocated with the convection center instead of being northwards. This affected the northward propagation of MISO. The easterly shear to the north of the equator was better simulated by the coarser resolution models than CFS T382. The low level specific humidity showed improvement only in CFS T382 until ~15° N. The analyses of the vertical profiles of moisture and its relation to rainfall revealed that all CFSv2 resolutions had a lower level of moisture in the lower level (< 850 hPa) and a drier level above. This eventually hampered the growth of deep convection in the model. These model shortcomings indicate a possible need of improvement in moist process parameterization in the model in tune with the increase in resolution.

Processes Associated with Convection Initiation in the North American Mesoscale Forecast System, Version 3 (NAMv3)

Abstract In support of the Next Generation Global Prediction System (NGGPS) project, processes leading to convection initiation in the North American Mesoscale Forecast System, version 3 (NAMv3) are explored. Two severe weather outbreaks—occurring over the southeastern United States on 28 April 2014 and the central Great Plains on 6 May 2015—are forecast retrospectively using the NAMv3 CONUS (4 km) and Fire Weather (1.33 km) nests, each with 5-min output. Points of convection initiation are identified, and patterns leading to convection initiation in the model forecasts are determined. Results indicate that in the 30 min preceding convection initiation at a grid point, upward motion at low levels of the atmosphere enables a parcel to rise to its level of free convection, above which it is accelerated by the buoyancy force. A moist absolutely unstable layer (MAUL) typically is produced at the top of the updraft. However, when strong updrafts are collocated with large vertical gradients of potential temperature and moisture, noisy vertical profiles of temperature, moisture, and hydrometeor concentration develop beneath the rising MAUL. The noisy profiles found in this study are qualitatively similar to those that resulted in NAMv3 failures during simulations of Hurricane Joaquin in 2015. The CM1 cloud model is used to reproduce these noisy profiles, and results indicate that the noise can be mitigated by including explicit vertical diffusion in the model. Left unchecked, the noisy profiles are shown to impact convective storm features such as cold pools, precipitation, updraft helicity intensity and tracks, and the initiation of spurious convection.

Diabatic Heating and Convective Asymmetries During Rapid Intensity Changes of Tropical Cyclones Over North Indian Ocean

ABSTRACT Rapid changes in Tropical Cyclone (TC) structure and intensity are the most difficult aspects of TC forecasting. The intensity and structural changes associated with rapid intensification (RI) and rapid weakening (RW) instances of TCs of North Indian Ocean (NIO) are analysed based on 5 cases each for RI and RW. The vertical profiles of heat (Q1) and moisture (Q2) based on heat and moisture budget equations are analysed to bring out the role of diabatic heating during the RI and RW processes. The associated structural changes are analysed based on the Fourier first order wave number-1 asymmetry in the precipitation patterns associated with RI and RW instances. During RI process, there are 2 predominant Q1 maxima associated with latent heat release near the TC centre, one in the upper troposphere and the other in the lower troposphere. During RW process, there is a single dominant Q1 and Q2 maxima in the mid-troposphere. Regarding the structural changes, there is a pronounced front-back asymmetry in both RI and RW cases. The asymmetry amplitude in the inner core is lower in the case of RI events but, is quite high for RW events. Thus, RW events are characterised by large convective asymmetry.