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Lower tropospheric thermal structure greatly affects atmospheric boundary-layer (ABL) stability and mixing processes with the free troposphere. In particular, in polluted urban zones, ABL stratification becomes a key variable in air quality research. This study focuses on generating a climatology (1990–2017) of the seasonal variability of ABL thermal structure in Mexico City by way of radiosonde analysis. Thermal inversion intensity and frequency are shown to be greater during winter and spring, a behaviour which coincides with greater pollutant concentrations. Higher concentrations are found during the dry season (November to May) than during the rainy months. In addition, significantly higher than normal surface pollutant concentrations are found on days with simple thermal inversion layers as well as during multilayer inversion days. Furthermore, stable layers, determined by potential temperature, are found throughout the year but more frequently during winter, whereas stable layers based on the virtual potential temperature prevail all year. In regions of complex terrain, such multiple stable layers have also been identified by previous authors. Additionally, the most unstable surface layers (in which the bulk Richardson number ( $${Ri}_{\mathrm {B}}$$ ) is small) develop during the rainy season, whereas during winter there are more levels in the vertical column with higher $${Ri}_{\mathrm {B}}$$ values. Although the Mexico City ABL and pollution episodes have been widely studied, this represents the first long-term investigation to consider the thermal stability of the ABL. Therefore, the present study provides a baseline for further research employing different observational techniques and high-resolution numerical models.

The 2014–15 Observations and Modeling of the Green Ocean Amazon (GOAmazon) field campaign over the central Amazon near Manaus, Brazil, occurred in coordination with the larger Cloud Processes of the Main Precipitation Systems in Brazil: A Contribution to Cloud-Resolving Modeling and to the Global Precipitation Measurement (CHUVA) project across Brazil. These programs provide observations of convection over the central Amazon on diurnal to annual time scales. In this study, we address the question of how Kelvin waves, observed in satellite observations of deep cloud cover over the GOAmazon region during the 2014–15 time period, modulate the growth, type, and organization of convection over the central Amazon. The answer to this question has implications for improved predictability of organized systems over the region and representation of convection and its growth on local to synoptic scales in global models. Our results demonstrate that Kelvin waves are strong modulators of synoptic-scale low- to midlevel free-tropospheric moisture, integrated moisture convergence, and surface heat fluxes. These regional modifications of the environment impact the local diurnal cycle of convection, favoring the development of mesoscale convective systems. As a result, localized rainfall is also strongly modulated, with the majority of rainfall in the GOAmazon region occurring during the passage of these systems.

Land use land cover (LULC) significantly impacts local circulation in the Mexico Basin, particularly wind field circulations such as gap winds, convergence lines, and thermally induced upslope/downslope wind. A case study with a high-pressure system over the Mexico Basin isolates the influence of large-scale synoptic forcing. Numerical simulations reveal a wind system composed of meridional circulation and a zonal component. Thermal pressure gradients between the Mexico basin and its colder surroundings create near-surface convergence lines as part of the meridional circulation. Experiments show that the intensity and organization of meridional circulations and downslope winds increase when LULC changes from natural and cultivated land to urban. Zonal circulation exhibits a typical circulation pattern with the upslope flow and descending motion in the middle of the basin. Large values of moist static energy are near the surface where air parcels pick up energy from the surface either as fluxes of enthalpy or latent heat. Surface heat fluxes and stored energy in the ground are drivers of local circulation, which is more evident in zonal circulation patterns.

Convective processes in the atmosphere over the Maritime Continent and their diurnal cycles have important repercussions for the circulations in the tropics and beyond. In this work, we present a new dataset of precipitable water vapor (PWV) obtained from the Sumatran GPS Array (SuGAr), a dense network of GPS stations principally for examining seismic and tectonic activity along the western coast of Sumatra and several offshore islands. The data provide an opportunity to examine the characteristics of convection over the area in greater detail than before. In particular, our results show that the diurnal cycle of PWV on Sumatra has a single late afternoon peak, while that offshore has both a midday and a nocturnal peak. The SuGAr data are in good agreement with GPS radio occultation data from the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) mission, as well as with imaging spectrometer data from the Ozone Measuring Instrument (OMI). A comparison between SuGAr and the NASA Water Vapor Project (NVAP), however, shows significant differences, most likely due to discrepancies in the temporal and spatial resolutions. To further understand the diurnal cycle contained in the SuGAr data, we explore the impact of the Madden–Julian oscillation (MJO) on the diurnal cycle with the aid of the Weather Research and Forecasting (WRF) Model. Results show that the daily mean and the amplitude of the diurnal cycle appear smaller during the suppressed phase relative to the developing/active MJO phase. Furthermore, the evening/nighttime peaks of PWV offshore appear later during the suppressed phase of the MJO compared to the active phase.

Isoprene fluxes vary seasonally with changes in environmental factors (e.g., solar radiation and temperature) and biological factors (e.g., leaf phenology). However, our understanding of the seasonal patterns of isoprene fluxes and the associated mechanistic controls is still limited, especially in Amazonian evergreen forests. In this paper, we aim to connect intensive, field-based measurements of canopy isoprene flux over a central Amazonian evergreen forest site with meteorological observations and with tower-mounted camera leaf phenology to improve our understanding of patterns and causes of isoprene flux seasonality. Our results demonstrate that the highest isoprene emissions are observed during the dry and dry-to-wet transition seasons, whereas the lowest emissions were found during the wet-to-dry transition season. Our results also indicate that light and temperature cannot totally explain isoprene flux seasonality. Instead, the camera-derived leaf area index (LAI) of recently mature leaf age class (e.g., leaf ages of 3–5 months) exhibits the highest correlation with observed isoprene flux seasonality (R2=0.59, p). Attempting to better represent leaf phenology in the Model of Emissions of Gases and Aerosols from Nature (MEGAN 2.1), we improved the leaf age algorithm by utilizing results from the camera-derived leaf phenology that provided LAI categorized into three different leaf ages. The model results show that the observations of age-dependent isoprene emission capacity, in conjunction with camera-derived leaf age demography, significantly improved simulations in terms of seasonal variations in isoprene fluxes (R2=0.52, p). This study highlights the importance of accounting for differences in isoprene emission capacity across canopy leaf age classes and identifying forest adaptive mechanisms that underlie seasonal variation in isoprene emissions in Amazonia.

A lightning–precipitation relationship (LPR) is studied at high temporal and spatial resolution (5 min and 5 km). As a proof of concept of these methods, precipitation data are retrieved from the National Severe Storms Laboratory (NSSL) NMQ product for southern Arizona and western Texas while lightning data are provided by the National Lightning Detection Network (NLDN). A spatial- and time-invariant (STI) linear model that considers spatial neighbors and time lags is proposed. A data denial analysis is performed over Midland, Texas (a region with good sensor coverage), with this STI model. The LPR is unchanged and essentially equal, regardless of the domain (denial or complete) used to obtain the STI model coefficients. It is argued that precipitation can be estimated over regions with poor sensor coverage (i.e., southern Arizona) by calibrating the LPR over well-covered domains that are experiencing similar storm conditions. To obtain a lightning-estimated precipitation that dynamically updates the model coefficients in time, a Kalman filter is applied to the STI model. The correlation between the observed and estimated precipitation is statistically significant for both models, but the Kalman filter provides a better precipitation estimation. The best demonstration of this application is a heavy-precipitation, high-frequency lightning event in southern Arizona over a region with poor radar and rain gauge coverage. By calibrating the Kalman filter over a data-covered domain, the lightning-estimated precipitation is considerably greater than that estimated by radar alone. Therefore, for regions where both rain gauge and radar data are compromised, lightning provides a viable alternative for improving QPE.

Long-term changes in North American monsoon (NAM) precipitation intensity in the southwestern United States are evaluated through the use of convective-permitting model simulations of objectively identified severe weather events during “historical past” (1950–70) and “present day” (1991–2010) periods. Severe weather events are the days on which the highest atmospheric instability and moisture occur within a long-term regional climate simulation. Simulations of severe weather event days are performed with convective-permitting (2.5 km) grid spacing, and these simulations are compared with available observed precipitation data to evaluate the model performance and to verify any statistically significant model-simulated trends in precipitation. Statistical evaluation of precipitation extremes is performed using a peaks-over-threshold approach with a generalized Pareto distribution. A statistically significant long-term increase in atmospheric moisture and instability is associated with an increase in extreme monsoon precipitation in observations and simulations of severe weather events, corresponding to similar behavior in station-based precipitation observations in the Southwest. Precipitation is becoming more intense within the context of the diurnal cycle of convection. The largest modeled increases in extreme-event precipitation occur in central and southwestern Arizona, where mesoscale convective systems account for a majority of monsoon precipitation and where relatively large modeled increases in precipitable water occur. Therefore, it is concluded that a more favorable thermodynamic environment in the southwestern United States is facilitating stronger organized monsoon convection during at least the last 20 years.

Bias and spread in Coupled Model Intercomparison Project Phase 5 simulated vertical specific humidity (q) structure are examined and related to both precipitation and column water vapor (cwv) near Manaus, Brazil, site of the recent Green Ocean Amazon (GOAmazon) campaign. Simulated seasonal-mean q profiles are typically too dry, especially at low levels and during the local dry season, consistent with previously identified surface hydroclimate biases in the Amazon. Multimodel empirical orthogonal function analysis of the models’ monthly climatological q profiles indicates two significant modes of ensemble spread in moisture vertical structure, with the leading mode peaked at low levels and the second mode in the lower free troposphere (LFT). While both modes project onto simulated cwv spread, only the first projects on precipitation, suggesting inconsistent sensitivity of simulated rainfall to LFT moisture. Relative to observations, models with high cwv and low-level moisture errors tend to exhibit high precipitation error.

This paper examines the ability of the Weather Research and Forecasting model forecast to simulate moisture and precipitation during the North American Monsoon GPS Hydrometeorological Network field campaign that took place in 2017. A convective-permitting model configuration performs daily weather forecast simulations for northwestern Mexico and southwestern United States. Model precipitable water vapor (PWV) exhibits wet biases greater than 0.5 mm at the initial forecast hour, and its diurnal cycle is out of phase with time, compared to observations. As a result, the model initiates and terminates precipitation earlier than the satellite and rain gauge measurements, underestimates the westward propagation of the convective systems, and exhibits relatively low forecast skills on the days where strong synoptic-scale forcing features are absent. Sensitivity analysis shows that model PWV in the domain is sensitive to changes in initial PWV at coastal sites, whereas the model precipitation and moisture flux convergence (QCONV) are sensitive to changes in initial PWV at the mountainous sites. Improving the initial physical states, such as PWV, potentially increases the forecast skills.

Almost one-half of the annual precipitation in the southwestern United States occurs during the North American monsoon (NAM). Given favorable synoptic-scale conditions, organized monsoon thunderstorms may affect relatively large geographic areas. Through an objective analysis of atmospheric reanalysis and observational data, the dominant synoptic patterns associated with NAM extreme events are determined for the period from 1993 to 2010. Thermodynamically favorable extreme-weather-event days are selected on the basis of atmospheric instability and precipitable water vapor from Tucson, Arizona, rawinsonde data. The atmospheric circulation patterns at 500 hPa associated with the extreme events are objectively characterized using principal component analysis. The first two dominant modes of 500-hPa geopotential-height anomalies of the severe-weather-event days correspond to type-I and type-II severe-weather-event patterns previously subjectively identified by Maddox et al. These patterns reflect a positioning of the monsoon ridge to the north and east or north and west, respectively, from its position in the “Four Corners” region during the period of the climatological maximum of monsoon precipitation from mid-July to mid-August. An hourly radar–gauge precipitation product shows evidence of organized, westward-propagating convection in Arizona during the type-I and type-II severe weather events. This new methodological approach for objectively identifying severe weather events may be easily adapted to inform operational forecasting or analysis of gridded climate data.

Northwestern Mexico experiences large variations in water vapor on seasonal time scales in association with the North American monsoon, as well as during the monsoon associated with upper-tropospheric troughs, mesoscale convective systems, tropical easterly waves, and tropical cyclones. Together these events provide more than half of the annual rainfall to the region. A sufficient density of meteorological observations is required to properly observe, understand, and forecast the important processes contributing to the development of organized convection over northwestern Mexico. The stability of observations over long time periods is also of interest to monitor seasonal and longer-time-scale variability in the water cycle. For more than a decade, the U.S. Global Positioning System (GPS) has been used to obtain tropospheric precipitable water vapor (PWV) for applications in the atmospheric sciences. There is particular interest in establishing these systems where conventional operational meteorological networks are not possible due to the lack of financial or human resources to support the network. Here, we provide an overview of the North American Monsoon GPS Transect Experiment 2013 in northwestern Mexico for the study of mesoscale processes and the impact of PWV observations on high-resolution model forecasts of organized convective events during the 2013 monsoon. Some highlights are presented, as well as a look forward at GPS networks with surface meteorology (GPS-Met) planned for the region that will be capable of capturing a wider range of water vapor variability in both space and time across Mexico and into the southwestern United States.

The relationships between the onset of tropical deep convection, column water vapor (CWV), and other measures of conditional instability are analyzed with 2 yr of data from the DOE Atmospheric Radiation Measurement (ARM) Mobile Facility in Manacapuru, Brazil, as part of the Green Ocean Amazon (GOAmazon) campaign, and with 3.5 yr of CWV derived from global positioning system meteorology at a nearby site in Manaus, Brazil. Important features seen previously in observations over tropical oceans—precipitation conditionally averaged by CWV exhibiting a sharp pickup at high CWV, and the overall shape of the CWV distribution for both precipitating and nonprecipitating points—are also found for this tropical continental region. The relationship between rainfall and CWV reflects the impact of lower-free-tropospheric moisture variability on convection. Specifically, CWV over land, as over ocean, is a proxy for the effect of free-tropospheric moisture on conditional instability as indicated by entraining plume calculations from GOAmazon data. Given sufficient mixing in the lower troposphere, higher CWV generally results in greater plume buoyancies through a deep convective layer. Although sensitivity of buoyancy to other controls in the Amazon is suggested, such as boundary layer and microphysical processes, the CWV dependence is consistent with the observed precipitation onset. Overall, leading aspects of the relationship between CWV and the transition to deep convection in the Amazon have close parallels over tropical oceans. The relationship is robust to averaging on time and space scales appropriate for convective physics but is strongly smoothed for averages greater than 3 h or 2.5°.

Transient inverted troughs (IVs) are a trigger for severe weather during the North American monsoon (NAM) in the southwest contiguous United States (CONUS) and northwest Mexico. These upper-tropospheric disturbances enhance the synoptic-scale and mesoscale environment for organized convection, increasing the chances for microbursts, straight-line winds, blowing dust, and flash flooding. This work considers changes in the track density climatology of IVs between 1951 and 2010. IVs are tracked as potential vorticity (PV) anomalies on the 250-hPa surface from a regional climate model that dynamically downscales the NCEP–NCAR Reanalysis 1. Late in the NAM season, a significant increase in IV track density over the 60-yr period is observed over Southern California and western Arizona, coupled with a slight decrease over northwest Mexico. Changes in precipitation are evaluated on days when an IV is observed and days without an IV, using high-resolution model-simulated precipitation estimates and CPC gridded precipitation observations. Because of changes in the spatial distribution of IVs during the 1951–2010 analysis period, which are associated with a strengthening of the monsoon ridge, it is suggested that IVs have played a lesser role in the initiation and organization of monsoon convection in the southwest CONUS during recent warm seasons.

During the North American monsoon global positioning system (GPS) Transect Experiment 2013, daily convective-permitting WRF simulations are performed in northwestern Mexico and the southern Arizona border region using the operational Global Forecast System (GFS) and North American Mesoscale Forecast System (NAM) models as lateral boundary forcing and initial conditions. Compared to GPS precipitable water vapor (PWV), the WRF simulations display a consistent moist bias in the initial specification of PWV leading to convection beginning 3–6 h early. Given appreciable observed rainfall, days are classified as strongly and weakly forced based only on the presence of an inverted trough (IV); gulf surges did not noticeably impact the development of mesoscale convective systems (MCSs) and related convection in northwestern Mexico. Strongly forced days display higher modeled precipitation forecast skill than weakly forced days in the slopes of the northern Sierra Madre Occidental (SMO) away from the crest, especially toward the west where MCSs account for the greatest proportion of all monsoon-related precipitation. A case study spanning 8–10 July 2013 illustrates two consecutive days when nearly identical MCSs evolved over northern Sonora. Although a salient MCS is simulated on the strongly forced day (9–10 July 2013) when an IV is approaching the core monsoon region, a simulated MCS is basically nonexistent on the weakly forced day (8–9 July 2013) when the IV is farther away. The greater sensitivity to the initial specification of PWV in the weakly forced day suggests that assimilation of GPS-derived PWV for these types of days may be of greatest value in improving model precipitation forecasts.

The complex interactions between water vapor fields and deep atmospheric convection remain one of the outstanding problems in tropical meteorology. The lack of high spatial–temporal resolution, all-weather observations in the tropics has hampered progress. Numerical models have difficulties, for example, in representing the shallow-to-deep convective transition and the diurnal cycle of precipitation. Global Navigation Satellite System (GNSS) meteorology, which provides all-weather, high-frequency (5 min), precipitable water vapor estimates, can help. The Amazon Dense GNSS Meteorological Network experiment, the first of its kind in the tropics, was created with the aim of examining water vapor and deep convection relationships at the mesoscale. This innovative, Brazilian-led international experiment consisted of two mesoscale (100 km × 100 km) networks: 1) a 1-yr (April 2011–April 2012) campaign (20 GNSS meteorological sites) in and around Manaus and 2) a 6-week (June 2011) intensive campaign (15 GNSS meteorological sites) in and around Belem, the latter in collaboration with the Cloud Processes of the Main Precipitation Systems in Brazil: A Contribution to Cloud-Resolving Modeling and to the Global Precipitation Measurement (CHUVA) Project in Brazil. Results presented here from both networks focus on the diurnal cycle of precipitable water vapor associated with sea-breeze convection in Belem and seasonal and topographic influences in and around Manaus. Ultimately, these unique observations may serve to initialize, constrain, or validate precipitable water vapor in high-resolution models. These experiments also demonstrate that GNSS meteorology can expand into logistically difficult regions such as the Amazon. Other GNSS meteorology networks presently being constructed in the tropics are summarized.

Deep atmospheric convection, which covers a large range of spatial scales during its evolution, continues to be a challenge for models to replicate, particularly over land in the tropics. Specifically, the shallow-to-deep convective transition and organization on the mesoscale are often not properly represented in coarse-resolution models. High-resolution models offer insights on physical mechanisms responsible for the shallow-to-deep transition. Model verification, however, at both coarse and high resolution requires validation and, hence, observational metrics, which are lacking in the tropics. Here a straightforward metric derived from the Amazon Dense GNSS Meteorological Network (~100 km × 100 km) is presented based on a spatial correlation decay time scale during convective evolution on the mesoscale. For the shallow-to-deep transition, the correlation decay time scale is shown to be around 3.5 h. This novel result provides a much needed metric from the deep tropics for numerical models to replicate.

Author(s): Serr, YL; Haase, JS; Adams, DK; Fu, Q; Ackerman, TP; Alexander, MJ; Arellano, A; Back, L; Emanuel, K; Kuang, Z; Mapes, B; Neelin, D; Raymond, D; Sobel, AH; Staten, PW; Subramanian, A; Thompson, DWJ; Vecchi, G; Wood, R; Zuidema, P

We assessed the temporal evolution of vegetation activity of mangroves in the Southeastern coastal of the Gulf of California (Mexico) through a multi-temporal analysis of Landsat TM images from 1990 to 2010 where time series of the Normalized Difference Vegetation Index (NDVI) were obtained. A multivariate regression analysis showed the presence of statistically significant negative trends of NDVI (low vegetation activity) in the coverage of mangrove forest, mangrove forest with pickleweed, and pickleweed; however, we did not found any meteorological variable (built time series of average minimum and maximum temperatures, and of accumulated rainfall) that controlled the observed trends. A pixel-by-pixel spatially distributed analysis of the temporal trends of NDVI, complemented by digitalization through photo interpretation of the shrimp farms present in the study area, showed a spatial relationship between the zones of greatest loss of vegetation activity (1990–2010) and the areas with greater proliferation of shrimp farms in the study area. Our study demonstrated the applicability of NDVI for the environmental assessment of mangroves. The relationship between changes in remote sensing indices and environmental variables allows for an efficient evaluation of the main environmental impacts, which can be used for coastal planning and management., The research was supported by the Mexican Secretary of Natural Resources and Environment (SEMARNAT-CONACYT; grant number SEMARNAT-2002-C01-0147). SM-B and RJM are students of Academic Program of Geoinformatics (UACJ).