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The Tropical Rainfall Measuring Mission (TRMM) Kwajalein Experiment (KWAJEX) was designed to obtain an empirical physical characterization of precipitating convective clouds over the tropical ocean. Coordinated datasets were collected by three aircraft, one ship, five upper-air sounding sites, and a variety of continuously recording remote and in s...
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The objective of this work was to determine the degree to which estuarine water parameters of the lower Chesapeake Bay area could be correlated to LANDSAT-1 multispectral imagery; and using successful correlation, demonstrate the feasibility of constructing synoptic water-parameter maps directly from satellite imagery. Chlorophyll, particulate coun...
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... The feature detection method described in this paper to identify locally enhanced reflectivity features in cool-season precipitation systems is built upon the implementation of adaptive thresholds for objective convective-stratiform precipitation classification developed for warm-season storms in a series of papers by Churchill and Houze (1984), Steiner et al. (1995), Houze (1997), andYuter et al. (2005). The underlying idea, identifying the cores of features that exceed the background value by an amount that varies with the background value, is well established (Steiner et al., 1995). ...
... Other similar equations with a decreasing threshold with increasing background value would also likely be suitable. The cosine scheme (Fig. 4a) is adapted from methods used to identify convective and stratiform precipitation structures in rain (e.g., Steiner et al., 1995;Yuter and Houze, 1997;Yuter et al., 2005;Powell et al., 2016). The choice of this specific equation is purposeful as it permits the same Python code to be used with an input field of radar reflectivity from a rain layer and appropriate parameter settings to exactly reproduce the data processing of the original C++ code used in Yuter et al. (2005). ...
... The cosine scheme (Fig. 4a) is adapted from methods used to identify convective and stratiform precipitation structures in rain (e.g., Steiner et al., 1995;Yuter and Houze, 1997;Yuter et al., 2005;Powell et al., 2016). The choice of this specific equation is purposeful as it permits the same Python code to be used with an input field of radar reflectivity from a rain layer and appropriate parameter settings to exactly reproduce the data processing of the original C++ code used in Yuter et al. (2005). Figure 4a shows how changing the maximum difference (a in Eq. 1; horizontal dashed line) and zero difference cosine value (b in Eq. 1; vertical dashed line where the function would cross the x axis) changes the overall shape of the difference function and thus the thresholds used to identify pixels that are cores. ...
Radar observations of winter storms often exhibit locally enhanced linear features in reflectivity, sometimes labeled as snow bands. We have developed a new, objective method for detecting locally enhanced echo features in radar data from winter storms. In comparison to convective cells in warm season precipitation, these features are usually less distinct from the background echo and often have more fuzzy or feathered edges. This technique identifies both prominent, strong features and more subtle, faint features. A key difference from previous radar reflectivity feature detection algorithms is the combined use of two adaptive differential thresholds, one that decreases with increasing background values and one that increases with increasing background values. The algorithm detects features within a snow rate field rather than reflectivity and incorporates an underestimate and overestimate of feature areas to account for uncertainties in the detection. We demonstrate the technique on several examples from the US National Weather Service operational radar network. The feature detection algorithm is highly customizable and can be tuned for a variety of data sets and applications.
... This is the region where MRG is climatologically most active in the tropics (e.g., Hendon & Liebmann, 1991;Kiladis et al., 2016). Six-hourly balloon soundings were made in a coordinated network of measurement stations (Figure 1a), along with other meteorological measurements from aircraft, ships, as well as remote and in situ surface-based sensors (Schumacher et al., 2007(Schumacher et al., , 2008Sobel et al., 2004;Yuter et al., 2005). Surface rain data were averaged from the gridded S-Pol radar retrievals and gauge measurements. ...
Convectively coupled equatorial waves are a significant source of atmospheric variability in the tropics. Current numerical models continue to struggle in simulating the coupled diabatic heating fields that are responsible for the development and maintenance of these waves. This study investigates how the diabatic fields associated with Mixed Rossby-Gravity waves (MRGs) are represented in four reanalysis products by using a unique observational dataset from the TRMM-KWAJEX (Tropical Rainfall Measuring Mission-Kwajalein Experiment) field campaign. These reanalyses include ERA5, Japanese 55-year Reanalysis (JRA-55), Climate Forecast System Reanalysis (CFSR), and Modern-Era Retrospective Analysis for Research and Applications (MERRA). We found that all four reanalyses captured the MRG structures in winds and temperature , and to a lesser degree in the humidity field except in the boundary layer. However, only the ERA5 and MERRA reanalyses captured the gradual rise and succession of the diabatic heating from boundary layer turbulence, shallow convection, cumulus congestus, and deep convection within the waves. ERA5 is the only product that also captured the gradual rise of the subgrid-scale vertical transport of moist static energy. All reanalysis products underestimated the diabatic heating from cumulus congestus. Results provide observational basis on what aspects of MRG can be trusted and what cannot in the reanalysis products. K E Y W O R D S convectively coupled equatorial waves, diabatic heating, mixed Rossby-Gravity wave, reanalysis products, TRMM-KWAJEX
... Land and oceanic regimes show different tendencies in reflectivity variation near the ground (Cifelli et al. 2007;Johnson et al. 2005;and Yuter et al. 2005;Zipser and Lutz 1994). Ground-based and satellite-based radar shows that, in general, the reflectivity below the 500-m altitude increases towards the ocean surface. ...
Slopes of radar reflectivity below freezing height (FZH) is a critical parameter to estimate the correct rainfall near the surface. TRMM PR-based radar reflectivity slopes are presented here, during Indian and Austral summer monsoon by calculating them in the lower troposphere (< 4 km). Slopes are either positive or negative, which means that the radar reflectivity either decreases (positive slopes) or increases (negative slopes) towards the surface. In the majority of cases, slopes decrease towards the surface over land, but over oceans increase towards the surface. In general, the slopes in convective tropical precipitation are negative where the Arabian Ocean has the highest fraction (~89%) of negative slopes. However, Bay of Bengal has the highest fraction of positive slopes (~21%). Western Himalaya Foothills has the highest fraction of positive slopes in convective precipitation and shows that ~76% and ~83% of convective and stratiform profiles decrease towards the surface. During the Austral summer monsoon, the Maritime Continent has the highest fraction of negative slopes (~92%) in convective precipitation followed by the Equatorial Indian Ocean. Land vs ocean and regional differences in radar reflectivity slopes are higher in convective precipitation compared to stratiform precipitation. Vertical profiles with extreme positive (>1 dBZ/km) slopes have higher echo top height (ETH) in convective precipitation over the tropical ocean during both the seasons, whereas over land, higher ETHs are associated with negative slopes.
... This is the region where MRG is climatologically most active in the tropics (e.g., Hendon & Liebmann, 1991;Kiladis et al., 2016). Six-hourly balloon soundings were made in a coordinated network of measurement stations (Figure 1a), along with other meteorological measurements from aircraft, ships, as well as remote and in situ surface-based sensors (Schumacher et al., 2007(Schumacher et al., , 2008Sobel et al., 2004;Yuter et al., 2005). Surface rain data were averaged from the gridded S-Pol radar retrievals and gauge measurements. ...
Convectively coupled equatorial waves are a significant source of atmospheric variability in the tropics. Current numerical models continue to struggle in simulating the coupled diabatic heating fields that are responsible for the development and maintenance of these waves. This study investigates how the diabatic fields associated with Mixed Rossby–Gravity waves (MRGs) are represented in four reanalysis products by using a unique observational dataset from the TRMM‐KWAJEX (Tropical Rainfall Measuring Mission‐Kwajalein Experiment) field campaign. These reanalyses include ERA5, Japanese 55‐year Reanalysis (JRA‐55), Climate Forecast System Reanalysis (CFSR), and Modern‐Era Retrospective Analysis for Research and Applications (MERRA). We found that all four reanalyses captured the MRG structures in winds and temperature, and to a lesser degree in the humidity field except in the boundary layer. However, only the ERA5 and MERRA reanalyses captured the gradual rise and succession of the diabatic heating from boundary layer turbulence, shallow convection, cumulus congestus, and deep convection within the waves. ERA5 is the only product that also captured the gradual rise of the subgrid‐scale vertical transport of moist static energy. All reanalysis products underestimated the diabatic heating from cumulus congestus. Results provide observational basis on what aspects of MRG can be trusted and what cannot in the reanalysis products.
... A horizontal cross section through the MC3E simulation taken at 12-km height allows us to appreciate the inhomogeneity of this convective storm that presented vigorous updrafts within only a few hundred meters of downdrafts ( Figure 1A). -A Regional Atmospheric Modeling System (RAMS, v6.2.05; Cotton et al., 2003;Storer and Posselt, 2019) simulation of the deep convection cases of 11 th and 17 th August 1999, that took place during the Kwajalein Experiment (KWAJEX, Yuter et al., 2005) and of the weakly organized oceanic convection case of 3 February 1999, that took place during the Tropical Rainfall Measuring Mission-Large Scale Biosphere-Atmosphere Experiment (TRMM-LBA, Silva Dias et al., 2002). A horizontal cross section through the TRMM-LBA simulation taken at 10 km height allows us to appreciate the inhomogeneity of individual coherent updraft structures that formed in this storm ( Figure 1B). ...
Convective motions and hydrometeor microphysical properties are highly sought-after parameters for evaluating atmospheric numerical models. With most of Earth's surface covered by water, space-borne Doppler radars are ideal for acquiring such measurements at a global scale. While these systems have proven to be useful tools for retrieving cloud microphysical and dynamical properties from the ground, their adequacy and specific requirements for spaceborne operation still need to be evaluated. Comprehensive forward simulations enable us to assess the advantages and drawbacks of six different Doppler radar architectures currently planned or under consideration by space agencies for the study of cloud dynamics. Radar performance is examined against the state-of-the-art numerical model simulations of well-characterized shallow and deep, continental, and oceanic convective cases. Mean Doppler velocity (MDV) measurements collected at multiple frequencies (13, 35, and 94 GHz) provide complementary information in deep convective cloud systems. The high penetration capability of the 13 GHz radar enables to obtain a complete, albeit horizontally under-sampled, view of deep convective storms. The smaller instantaneous field of view (IFOV) of the 35 GHz radar captures more precise information about the location and size of convective updrafts above 5-8 km height of most systems which were determined in the portion of storms where the mass flux peak is typically located. Finally, the lower mean Doppler velocity uncertainty of displaced phase center antenna (DPCA) radars makes them an ideal system for studying microphysics in shallow convection and frontal systems, as well as ice and mixed-phase clouds. It is demonstrated that a 94 GHz DCPA system can achieve retrieval errors as low as 0.05-0.15 mm for raindrop volume-weighted mean diameter and 25% for rime fraction (for a −10 dBZ echo).
... Other papers have described how "external" forced ascent from large-scale convergence or mesoscale circulations, as opposed to features such as cold pools that are directly driven by convection itself, can initiate deep convection (e.g., Krueger 1988;Parsons et al. 1991;Xu et al. 1992;Bluestein and Parker 1993;Mapes and Houze 1995;Ziegler et al. 1997;Pielke 2001;Trier et al. 2004;Yuter et al. 2005;Masunaga and Kummerow 2006;Garcia-Carreras et al. 2011;Rieck et al. 2014;Rousseau-Rizzi et al. 2017). Besides mountainous terrain, other surface heterogeneities (e.g., in moisture) can promote horizontal gradients in virtual potential temperature and drive solenoidal circulations that initiate deep convection (e.g., Trier et al. 2004). ...
This study examines two factors impacting initiation of moist deep convection: free tropospheric environmental relative humidity ( ϕ E ) and horizon scale of sub-cloud ascent ( R sub ), the latter exerting a dominant control on cumulus cloud width. A simple theoretical model is used to formulate a “scale selection” hypothesis: that a minimum R sub is required for moist convection to go deep, and that this minimum scale decreases with increasing ϕ E . Specifically, the ratio of to saturation deficit (1– ϕ E ) must exceed a certain threshold value that depends on cloud-layer environmental lapse rate. Idealized, large-eddy simulations of moist convection forced by horizontally-varying surface fluxes show strong sensitivity of maximum cumulus height to both ϕ E and R sub consistent with the hypothesis. Increasing R sub by only 300-400 m can lead to a large increase (> 5 km) in cloud height. A passive tracer analysis shows that the bulk fractional entrainment rate decreases rapidly with R sub but depends little on ϕ E . However, buoyancy dilution increases as either R sub or ϕ E decreases; buoyancy above the level of free convection is rapidly depleted in dry environments when R sub is small. While deep convective initiation occurs with an increase in relative humidity of the near environment from moistening by earlier convection, the importance of this moisture preconditioning is inconclusive as it is accompanied by an increase in R sub . Overall, it is concluded that small changes to R sub driven by external forcing or by convection itself could be a dominant regulator of deep convective initiation.
... Similar to Chen et al. (2017), the observations from the surface-based Kwajalein S-band radar, with a wavelength of 10.71 cm and a coverage radius of 150 km, are used for model validation in this study. The black dot in Figure 1 denotes the location of the radar apparatus, whose details were previously summarized by Yuter et al. (2005). Figure 3 shows a comparison of the normalized frequency of the radar-estimated rain and simulated rain. ...
... The KWAJEX radar data used for validation of the model results can be downloaded from the Goddard Earth Sciences Data and Information Services Center (GES DISC): https://disc2.gesdisc.eosdis.nasa.gov/data/TRMM_GV_L2/TRMM_2A55UW.7/1999/231/ (Yuter et al., 2005). 42075073 ...
The parameterization of cloud entrainment rate has been problematic for many years, hindering the accurate representation of convective processes in large‐scale models. Here, we extend our previous work on individual shallow convection to ensemble deep convection. Entrainment rate is estimated based on three‐dimensional convective clouds from August 19 to 20, 1999, during the Kwajalein Experiment, simulated using a high‐resolution cloud‐resolving model. They are found to be negatively correlated with both vertical velocity and buoyancy, and positively correlated with the vertical divergence of the vertical velocity and with the reciprocal of cloud radii. The physical mechanisms underlying these relationships are interpreted. It is found that the parameterizations with multiple properties perform better than those with a single property. Entrainment rate and relative humidity of entrained air are positively correlated at temperature higher than 0°C, but negatively correlated at temperature lower than 0°C. Relative humidity is also included in the parameterization of entrainment rate, which differs from our previous work on shallow cumulus clouds and other studies. Finally, two forms of parameterization for entrainment rate are recommended. The first treats the entrainment rate as a function of the vertical velocity and buoyancy for temperature higher than 0°C, but as a function of relative humidity and buoyancy for temperature lower than 0°C. The second involves an equation that relates entrainment rate to vertical velocity and buoyancy regardless of temperature.
... Additional in situ measurements were made in the stratiform areas behind midlatitude and tropical MCSs during the 1999 Kwajalein Field Experiment (Yuter et al. 2005), the 2002 Cirrus Regional Study of Tropical Anvils and Cirrus Layers-Florida-Area Cirrus Experiment (Jensen et al. 2004), and the 2011 Midlatitude Continental Convective Clouds Experiment (MC3E; Jensen et al. 2016). Heymsfield et al. (2015) used data from all three experiments to show that the maximum particle size increased while descending through the ML, regardless of whether the environment was saturated or highly subsaturated. ...
This study examines microphysical and thermodynamic characteristics of the 20 June 2015 mesoscale convective system (MCS) observed during the Plains Elevated Convection at Night (PECAN) experiment, specifically within the transition zone (TZ), enhanced stratiform rain region (ESR), anvil region, melting layer (ML), and the rear inflow jet (RIJ). Analyses are developed from airborne optical array probe data and multiple-Doppler wind and reflectivity syntheses using data from the airborne NOAA Tail Doppler Radar (TDR) and ground-based Weather Surveillance Radar – 1988 Doppler (WSR-88D) radars.
Seven spiral ascents/descents of the NOAA P-3 aircraft were executed within various regions of the 20 June MCS. Aggregation modified by sublimation was observed in each MCS region, regardless of whether the sampling was within the RIJ. Sustained sublimation and evaporation of precipitation in subsaturated layers led to a trend of downward moistening across the ESR spirals, with greater degrees of subsaturation maintained when in the vicinity of the descending RIJ. In all cases where melting was observed, the ML acted as a prominent thermodynamic boundary, with differing rates of change in temperature and relative humidity above and below the ML. Two spiral profiles coincident with the rear inflow notch provided unique observations within the TZ and were interpreted in the context of similar observations from the 29 June 2003 Bow Echo and Mesoscale Convective Vortex Experiment MCS. There, sublimation cooling and enhanced descent within the RIJ allowed ice particles to survive to temperatures as warm as +6.8°C before completely sublimating/evaporating.
... Three control simulations are initialized from three soundings made during tropical field campaigns, each representing a different type of tropical environment. The 11 August and 17 August 1999 soundings were taken during the Kwajalein Experiment (KWAJEX: Yuter et al., 2005), and the 23 February 1999 sounding from TRMM-LBA (Tropical Rainfall Measuring Mission -Large Scale Biosphere-Atmosphere (Klemp and Wilhelmson, 1978) Surface: Ocean, SST held constant at observed surface temperature; LEAF3 (Walko et al., 2000) Model top: Rigid lid, Rayleigh friction layer of six levels Radiation Harrington (1997) Turbulence Smagorinsky (1963) Microphysics Two-moment bulk, eight hydrometeor species (Meyers et al., 1997;Saleeby and van den Heever, 2013) Experiment: Silva Dias et al., 2002) over the Amazon. The control simulations are described further in Section 3.1. ...
Though deep convective clouds are important to numerous aspects of the climate system, they remain a source of much uncertainty. The varying large‐scale dynamics, relatively short development time, range of spatial scales, and complex microphysics involved in deep convection lead to difficulty in both observation of these clouds and modelling their impacts on larger spatial and longer temporal scales. This study utilizes an ensemble of high‐resolution cloud‐resolving simulations of deep convection forming in a spectrum of different environments to explore the sensitivity of deep convection, and in particular convective mass flux to changes in the initial conditions. We find that convection strength is strongly sensitive to small perturbations in the environment, specifically the convective available potential energy (CAPE) and boundary‐layer humidity. That storm effects on the environment, such as mass transport and tropospheric moistening through detrainment, are sensitive to these initial conditions points to the importance of better representing such parameters in global models.
... While consistency in operations between the midlatitudes and tropics may reduce the number of guidelines needed to avoid convective hazards, it could also be inefficient for flight routing and planning when tropical convection is significantly different than midlatitude convection. It is commonly understood that there are some significant dynamical and physical differences between the convection populations that occur in the continental midlatitudes and maritime tropics (Chin et al. 1995;Liu and Zipser 2005;Yuter et al. 2005;Vant-Hull et al. 2016). Key differences in the convective populations that would impact the frequency and hazard potential of CIT include maximum vertical velocities of updrafts, the maximum height of convection and the probability of convection overshooting into the stratosphere, and the diurnal variation of convection. ...
Convectively induced turbulence (CIT) is an aviation hazard that continues to be a forecasting challenge as operational forecast models are too coarse to resolve turbulence affecting aircraft. In particular, little is known about tropical maritime CIT. In this study, a numerical simulation of a tropical oceanic CIT case where severe turbulence was encountered by a commercial aircraft is performed. The Richardson number (Ri), subgrid-scale eddy dissipation rate (EDR), and second-order structure functions (SF) are used as diagnostics to determine which may be used for CIT related to developing and mature convection. Model-derived subgrid-scale EDR in past studies of midlatitude continental CIT was shown to be a good diagnostic of turbulence but underpredicted turbulence intensity and areal coverage in this tropical simulation. SF diagnosed turbulence with moderate to severe intensity near convection and agreed most with observations. Further, SF were used to diagnose turbulence for developing convection. Results show that the areal coverage of turbulence associated with developing convection is less than mature convection. However, the intensity of turbulence in the vicinity of developing convection is greater than the turbulence intensity in the vicinity of mature convection highlighting developing convection as an additional concern to aviation.
