Precipitable Water Data Research Articles

Characteristics of precipitation pattern in the Arabian Peninsula and its variability associated with ENSO

A detailed analysis of the precipitation pattern of the Arabian Peninsula and its temporal and spatial variability were investigated in connection with ENSO. Also, the variability of precipitable water and circulation characteristics was examined for a better understanding. The study was carried out utilizing TRMM rainfall, NOAA OLR, precipitable water, wind, and humidity data sets. It is evident that Northern Arabian Peninsula receives high amount of rainfall mainly during winter and early summer (November to April) in connection with the passage of mid tropospheric westerly troughs and Mediterranean low-pressure systems. But the precipitation pattern over the Southern Arabian Peninsula reveals that it is mainly during summer (May to October) due to the Arabian Sea branch of monsoon and moisture laden cross equatorial LLJ flow. Further, analysis was carried out to assess the influence of ENSO on the precipitation pattern. Thorough analysis was carried out on the circulation pattern using velocity potential in the lower troposphere to understand the features of variability on Hadley/Walker circulation in relation with organized convection. El Nino and La Nina have profound influence on the rainfall pattern in a different manner in the Northern and Southern Arabian Peninsula. Large-scale circulation pattern as derived from velocity potential indicates that shifting of the rising/sinking limb of Hadley/Walker circulation associated with the ENSO causes variability in precipitation.

레이더 자료를 이용한 강우발생 판단의 정확도 향상을 위한 가강수량의 효용성평가

This study evaluated the accuracy of using the radar data to determine the rainfall occurrence, also the possibility of using the precipitable water as a role to improve the accuracy. To evaluate the role of precipitable water, the occurrence probability of rainfall was derived and evaluated. Also, how the precipitable water could improve the accuracy of rain/no rain decision was evaluated. In this study, Both the Gwanaksan radar data and 28 AWS within the radar umbrella were analyzed. The precipitable water data were derived by using the Osan and Baengnyeongdo aerological station data. The results are as follows: (1) When using the 1.5 km CAPPI data and PPI data, the matching probability of rainfall occurrence was calculated about 30% and the matching probability of no rainfall occurrence was calculated about 95%. (2) The precipitable water was found to improve the radar accuracy on rain/no rain decision by about 4% (monthly maximum: 8%). (3) This result was similarly calculated when using the 1.5 km CAPPI data and PPI data. (4) Precipitable water has a role of threshold that transform rain into no rain during the dry season and transform no rain into rain during the wet season.

Uncertainty in CMIP5 model-projected changes in the onset/retreat of the Australian summer monsoon

This study addresses several significant drawbacks in our previous analyses of how Australian summer monsoon onset/retreat may respond to global warming in CMIP3 model simulations. We have analysed daily 850 hPa wind, volumetric precipitable water, precipitation and temperature data from 26 CMIP5 models over a pair of 55-year simulations. Firstly, the CMIP5 models do not show significant improvement in capturing observed features of the monsoon onset/retreat in the region, despite of a slightly reduced bias in multi-model ensemble results. We show that wind–rainfall relationship varies with models and rainfall-based wet season onsets may not adequately represent the monsoon development. Under global warming, although 26-model averages show delayed onset and shortened duration, significant uncertainty exists: 10 models simulated delayed onset but it became earlier in another group of 7 models. Similar model discrepancies are seen in the modelled changes in retreat dates. The range of uncertainty in the projected changes is similar in CMIP3 and CMIP5 models and further analysis re-affirms previously proposed reasons: one is the different influence of a number of drivers in these models and the other is the different changes in these drivers themselves in future climate. Overall, most of the models showed impacts of ENSO and the Indian Ocean on the Australian summer monsoon onset/retreat, but the models differed quite significantly in the magnitude of such impacts. Another factor is different warming patterns and magnitudes in the tropical Pacific and Indian Oceans. When combined, the two provide a better explanation of the scatter among the 26 CMIP5 model results.

Determination of summer monsoon onset and its related large-scale circulation characteristics over Pakistan

The onset of summer monsoon over the Core Monsoon Region of Pakistan (CMRP) has been investigated in this study using observational daily rainfall and Precipitable Water (PW) data sets. An objective criterion is proposed to define monsoon onset dates by employing Precipitation Index and Normalized Precipitable Water Index techniques. The climatological mean summer monsoon onset dates over CMRP based on daily rainfall data sets are observed to be 1 July and 30 June in the station and gridded data sets, respectively. Whereas the daily PW-based climatological mean onset date is 30 June. The year-wise onset dates determined through station and gridded rainfall data sets are very similar but these dates differ in case of PW-based onsets. The evolution of large-scale circulation anomalies and thermodynamic structure leading monsoon onset over Pakistan shows that a strong positive temperature and geopotential height anomalies appear over the northwestern part of the core region in the upper atmosphere. This warm geopotential height anomaly gets strengthen as the monsoon onset approaches. The temperature anomalies are barotropic whereas the geopotential height anomalies are baroclinic with the presence of low level anticyclone over the Tibetan Plateau. A moisture convergence zone along the foothill of Himalayas and low level moisture convergence zone over the north Arabian Sea set the stage for the moisture carrying monsoon winds to blow inland towards CMRP. The moisture is mainly supplied from the Arabian Sea, as the low pressure system approaches CMRP from the Bay of Bengal.

Assessment of errors in Precipitable Water data derived from Global Navigation Satellite System observations

Among the new remote sensing techniques, one of the most promising is a GNSS meteorology, which provides continuous remote monitoring of the troposphere water vapor in all weather conditions with high temporal and spatial resolution. The Continuously Operating Reference Station (CORS) network and available meteorological instrumentation and models were scrutinized (we based our analysis on ASG-EUPOS network in Poland) as a troposphere water vapor retrieval system. This paper shows rigorous mathematical derivation of Precipitable Water errors based on uncertainties propagation method using all available data source quality measures (meteorological sensors and models precisions, ZTD estimation error, interpolation discrepancies, and ZWD to PW conversion inaccuracies). We analyze both random and systematic errors introduced by indirect measurements and interpolation procedures, hence estimate the PW system integrity capabilities. The results for PW show that the systematic errors can be under half-millimeter level as long as pressure and temperature are measured at the observation site. In other case, i.e. no direct observations, numerical weather model fields (we used in this study Coupled Ocean Atmospheric Mesoscale Prediction System) serves as the most accurate source of data. Investigated empirical pressure and temperature models, such as GPT2, GPT, UNB3m and Berg introduced into WV retrieval system, combined bias and random errors exceeding PW standard level of accuracy (3mm according to E-GVAP report). We also found that the pressure interpolation procedure is introducing over 0.5hPa bias and 1hPa standard deviation into the system (important in Zenith Total Delay reduction) and hence has negative impact on the WV estimation quality.

Clear sky irradiances using REST2 and MODIS

In order to simulate historical Global Horizontal Irradiance (GHI) and Direct Normal Irradiance (DNI) solar resources, a method using MODIS level 3 (L3) daily satellite data as input to the REST2 clear sky model is presented to derive clear sky solar irradiance for California. MODIS L3 precipitable water (PW) and especially aerosol optical depth (AOD) were found to be significantly biased and were therefore calibrated based on AOD and PW from Aerosol Robotic Network (AERONET) ground monitoring sites.For reference, MODIS input data was replaced by the following input data sources: 3 hourly PW and AOD from Monitoring Atmosphere Composition and Climate (MACC) and monthly climatological Linke Turbidity from Solar radiation Data (SoDa). Similarly, other clear sky models, specifically Ineichen and McClear, were also run for reference.Validation was conducted using irradiance anomalies defined as the difference between irradiance and its 15day moving average against ground measurement from California Irrigation Management Information System (CIMIS), National Renewable Energy Laboratory (NREL), and Integrated Surface Irradiance Study (ISIS) stations. It was found that the calibration of MODIS data markedly improves the accuracy of modeled GHI and DNI anomalies and REST2 clear sky model with calibrated MODIS data achieved the highest accuracy among all model and input data combinations. The improvement in accuracy of PW and AOD input data through calibration is relatively more important than the choice of clear sky model.

CMIP5 model-simulated onset, duration and intensity of the Asian summer monsoon in current and future climate

A number of significant weaknesses existed in our previous analysis of the changes in the Asian monsoon onset/retreat from coupled model intercomparison project phase 3 (CMIP3) models, including a lack of statistical significance tests, a small number of models analysed, and limited understanding of the causes of model uncertainties. Yet, the latest IPCC report acknowledges limited confidence for projected changes in monsoon onset/retreat. In this study we revisit the topic by expanding the analysis to a large number of CMIP5 models over much longer period and with more diagnoses. Daily 850 hPa wind, volumetric atmospheric precipitable water and rainfall data from 26 CMIP5 models over two sets of 50-year periods are used in this study. The overall model skill in reproducing the temporal and spatial patterns of the monsoon development is similar between CMIP3 and CMIP5 models. They are able to show distinct regional characteristics in the evolutions of Indian summer monsoon (ISM), East Asian summer monsoon (EASM) and West North Pacific summer monsoon (WNPSM). Nevertheless, the averaged onset dates vary significantly among the models. Large uncertainty exists in model-simulated changes in onset/retreat dates and the extent of uncertainty is comparable to that in CMIP3 models. Under global warming, a majority of the models tend to suggest delayed onset for the south Asian monsoon in the eastern part of tropical Indian Ocean and Indochina Peninsula and nearby region, primarily due to weakened tropical circulations and eastward shift of the Walker circulation. The earlier onset over the Arabian Sea and part of the Indian subcontinent in a number of the models are related to an enhanced southwesterly flow in the region. Weak changes in other domains are due to the offsetting results among the models, with some models showing earlier onsets but others showing delayed onsets. Different from the analysis of CMIP3 model results, this analysis highlights the importance of SST warming patterns over both the tropical Pacific and Indian Oceans in affecting the modelling results. The increased atmospheric moisture content offsets some effects of the delayed onset and results in increased rainfall intensity during the active monsoon period. The deficiencies of using rainfall alone in assessing the potential changes of the monsoon system are also shown in this study.

Detecting Long-Term Trends in Precipitable Water over the Tibetan Plateau by Synthesis of Station and MODIS Observations*

Abstract Long-term trends in precipitable water (PW) are an important component of climate change assessments for the Tibetan Plateau (TP). PW products from Moderate Resolution Imaging Spectroradiometer (MODIS) are able to provide good spatial coverage of PW over the TP but limited in time coverage, while the meteorological stations in the TP can estimate long-term PW but unevenly distributed. To detect the decadal trend in PW over the TP, Bayesian inference theory is used to construct long-term and spatially continuous PW data for the TP based on the station and MODIS observations. The prior information on the monthly-mean PW from MODIS and the 63 stations over the TP for 2000–06 is used to get the posterior probability knowledge that is utilized to build a Bayesian estimation model. This model is then operated to estimate continuous monthly-mean PW for 1970–2011 and its performance is evaluated using the monthly MODIS PW anomalies (2007–11) and annual GPS PW anomalies (1995–2011), with RMSEs below 0.65 mm, to demonstrate that the model estimation can reproduce the PW variability over the TP in both space and time. Annual PW series show a significant increasing trend of 0.19 mm decade−1 for the TP during the 42 years. The most significant PW increase of 0.47 mm decade−1 occurs for 1986–99 and an insignificant decrease occurs for 2000–11. From the comparison of the PW data from JRA-55, ERA-40, ERA-Interim, MERRA, NCEP-2, and ISCCP, it is found that none of them are able to show the actual long-term trends and variability in PW for the TP as the Bayesian estimation.

Impact study of integrated precipitable water estimated from Indian GPS measurements

The Global Positioning System – Integrated Precipitable Water (IPW) data from Indian stations namely Chennai, Guwahati, Kolkata, Mumbai and New Delhi have been assimilated in the National Centre for Medium Range Weather Forecasting’s (NCMRWF) Global Data Assimilation System (GDAS). Gridpoint Statistical Interpolation (GSI) Scheme of GDAS analysis is experimented with the global model T254L64. The analyses and forecasts are carried out at triangular truncation of wave number 254 and with 64 levels in vertical. Global analyses are carried four times (0000 UTC, 0600 UTC, 1200 UTC and 1800 UTC) daily with intermittent time scheme. Model integrations are carried up to 168 hours. The present study examines the impact that integrated precipitable water has over various meteorological parameters. The study reveals that the assimilation of IPW data influences the analyses and corresponding forecasts of the weather model T254L64. This is an attempt of assimilation of IPW data of the aforesaid five Indian stations in the global model and examination of corresponding impact on various meteorological parameters over Indian region. It is seen that for the layers above 750 hPa the zonal and meridional wind components for IPW analyses have less biases. Forecasts from IPW simulations are found to have consistently by lower 850 hPa wind vector root mean square error (RMSE) where as at 250 hPa, improvement in IPW runs are seen only for day-1 and day-4 forecasts. For temperature at 850 hPa, IPW forecasts valid for day-4 & day-5 are better. At 250 hPa, temperature RMSE for IPW runs is lower for day-1 forecasts. Mean error of IPW forecasts at 250 hPa is lower for all the days of forecasts. Also, geo-potential RMSE for the IPW runs at 250 hPa is lower for all the days of forecasts. Forecasts vs analyses study shows positive impact of IPW assimilation on the anomaly and pattern correlations.

Long-Lived Concentric Eyewalls in Typhoon Soulik (2013)

AbstractThe authors report on western North Pacific Typhoon Soulik (2013), which had two anomalously long-lived concentric eyewall (CE) episodes, as identified from microwave satellite data, radar data, and total precipitable water data. The first period was 25 h long and occurred while Soulik was at category 4 intensity. The second period was 34 h long and occurred when Soulik was at category 2 intensity. A large moat and outer eyewall width were present in both CE periods, and there was a significant contraction of the inner eyewall radius from the first period to the second period. The typhoon intensity decrease was partially due to encountering unfavorable environmental conditions of low ocean heat content and dry air, even though inner eyewall contraction would generally support intensification. The T–Vmax diagram (where T is the brightness temperature and Vmax is the best track–estimated intensity) is used to analyze the time sequence of the intensity and convective activity. The convective activity (and thus the integrated kinetic energy) increased during the CE periods despite the weakening of intensity.

A high-quality, homogenized, global, long-term (1993-2008) DORIS precipitable water data set for climate monitoring and model verification

For the first time a high-quality, consistent, global, long-term data set of zenith tropospheric delay (ZTD) and precipitable water (PW) is produced from Doppler orbitography radiopositioning integrated by satellite (DORIS) measurements at 81 sites. The data set was screened using a two-level procedure. First, postprocessing information is used to apply range checks and outlier checks to ZTD and formal error estimates. Second, outliers are detected by comparing DORIS ZTD with European Centre for Medium-Range Weather Forecasts reanalysis (ERA-Interim) data. These procedures reject 3% and 1% of the data, respectively. A linear drift is evidenced in the screened DORIS ZTD data compared to ERA-Interim and Global Positioning System (GPS) data, which potentially results from biases introduced by the progressive replacement of Alcatel antennas with Starec antennas. The DORIS PW is homogenized by applying a bias correction computed form comparison with ERA-Interim data each time station equipment is changed. The homogenized DORIS data are in excellent agreement with GPS data (correlation of 0.98 and standard deviation of differences of 1.5 kg m−2) and with ERA-Interim and satellite PW data (correlation > 0.95 and standard deviation of differences < 2.7 kg m−2). The agreement with radiosonde data is less good. Preliminary results of water vapor trends and variability are shown for 31 sites with more than 10 years of data. Good consistency is found between DORIS PW trends and ERA-Interim trends, which demonstrates the high potential of the DORIS PW data set for climate monitoring and model verification. The final DORIS PW data set is freely available in the supporting information.

Assimilation of GPS Radio Occultation Refractivity Data with WRF 3DVAR and Its Impact on the Prediction of a Heavy Rainfall Event

AbstractTo accommodate accurate analyses and forecasts of a heavy rainfall event over the Korean Peninsula, the authors assimilated the GPS radio occultation (RO) data by using the Weather Research and Forecasting Model (WRF) and its three-dimensional variational data assimilation system (3DVAR). The employed datasets are from the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) and Challenging Minisatellite Payload (CHAMP) missions. The selected case was from late October 2006, which intensively hit the northeastern part of the Korean Peninsula with record-breaking rainfall. In this study, the local refractivity observation operator was used in assimilating GPS RO soundings. The results are more pronounced for the cycling assimilation of GPS RO data than for the one-time data assimilation. From all of the parameters investigated (temperature, geopotential height, specific humidity, and winds), the GPS RO soundings highly modified the moisture distribution in the lower troposphere and also changed the wind field via the model dynamics. For the heavy rainfall forecast, the quantitative accuracy of the precipitation forecast with the GPS RO data assimilation was in good agreement with observations in terms of the maximum rainfall amount and threat scores. The improved forecast in the experiment came from the exact positioning of the low pressure system and its consequent convergence near the rainfall area. When RO data and GPS precipitable water data were assimilated simultaneously, the moisture distribution changed horizontally and vertically such that it increased the amount of rainfall, and an accurate description of the convective system development was feasible.

Impact of on-site atmospheric water vapor estimation methods on the accuracy of local solar irradiance predictions

Various types of precipitable water (PW) measurement are compared for different sites around Tucson, Arizona, where arid conditions prevail, and the sensitivity of irradiance to PW variations is largest. The accuracy of some determinations of this quantity is assessed by comparison with routine GPS meteorology data. Large scatter is obtained with all types of empirical functions relating PW to surface temperature and humidity data, but the climate sensitivity of this kind of determination is found lowest when relating PW to the surface specific humidity, rather than the more usual vapor pressure or dew point temperature. The impact on the accuracy of predicted direct normal irradiance (DNI) and global horizontal irradiance (GHI) of various sources of PW data, at either low or high temporal resolution, is assessed using predictions from the REST2 radiative model, in combination with co-located sunphotometric and radiometric data at Tucson during a 7-month period. Results suggest that the accuracy of the predicted DNI and GHI is only lightly sensitive to the uncertainty in the input PW data. In case PW is not measured locally, a convenient source of data is provided by reanalysis, such as from the MERRA model.

Impact of Turkish ground‐based GPS‐PW data assimilation on regional forecast: 8–9 March 2011 heavy snow case

AbstractA ground‐based Global Positioning System (GPS) network which consists of 147 uniformly distributed stations has been implemented in Turkey in May 2009. Precipitable water (PW) data estimated using this network is available since October 2010. Three experiments for a specific heavy snow case have been conducted to assess the impact of assimilation of this new data on short range weather forecasts. Verifications with surface observations indicate some reduction in the biases of basic variables for whole domain, and improvement in precipitation forecasts. Upper air variables of all simulations are compared with radiosonde observations, and some improvement in dew point temperatures through troposphere is observed with cycling mode assimilation.

Improved vegetation greenness increases summer atmospheric water vapor over Northern China

AbstractNorthern China is an environmentally vulnerable region with a severe water shortage. Several ecological restoration projects since the 1980s have greatly increased vegetation greenness. This study aims to assess the impacts of the changes in vegetation greenness from 1982 to 2008 on the atmospheric precipitable water (PW) over northern China during the summer seasons (June, July, and August) using reanalysis PW and satellite vegetation index data. Several statistical methods, such as linear regression (piecewise, stepwise), the Empirical Orthogonal Function, Pearson's correlation, have been used to explore the variations in and the coupling between vegetation greenness and PW. The influences of three major atmospheric circulations (the East Asian summer monsoon, the South Asian summer monsoon, and the Westerly circulation) have also been considered and excluded. The results show that summer vegetation greenness within the semi‐arid western part of northern China, including the Tibetan Plateau, is closely linked to summer PW after excluding the influences of atmospheric circulations and that vegetation accounts for as much as 30% of the total PW variance. Vegetation greenness in the central and western water‐limited regions of northern China has significant (&gt;90% confidence level) positive impacts on summer PW. After investigating the relationships among evapotranspiration (ET), PW, and precipitation, it was concluded that strengthened ET driven by increased vegetation greenness makes a major contribution to increased PW. Different from the earlier hypothesis in the literature “only intact contiguous cover of natural forest having extensive borders with larger water bodies is able to keep land moisten up to an optimal for life level everywhere on land,” this study found that interior forest covered regions with adequate water supply can also provide an ET benefit of PW for other regions. This study demonstrates the implications of large‐scale ecological restoration projects on the hydrological cycle in arid/semi‐arid regions.

Impact of SSM/I retrieval data on the systematic bias of analyses and forecasts of the Indian summer monsoon using WRF assimilation system

Assimilation and forecast experiments have been carried out in this study using conventional observations as well as total precipitable water and surface wind data retrieved from the Special Sensor for Microwave Imaginary (SSM/I) sensors. The main objectives of this study were to document the bias in short-range predictions of the Weather Research and Forecasting (WRF) version 3.1 model over the Indian region during the summer monsoon season and the impact of SSM/I data. All the experiments were carried out in the monsoon seasons of 2001 as a part of pilot phase studies for the South Asian Regional Reanalysis (SARR) project. It is seen that the model has strong bias in wind forecasts over the Arabian Sea and the Indian Ocean. A cyclonic bias in the forecasts exists over south-west India. Over the equatorial Indian Ocean, a strong southerly bias towards the Bay of Bengal is noticed. The model has a systematic bias to increase moisture over most parts of the equatorial Indian Ocean. Except over the Gangetic plains, the model exhibits dry bias with reduced moisture over most parts of India in 24 hour forecasts. The impact of assimilation of SSM/I products has been to increase the moisture over the Bay of Bengal, where the model has shown dry bias. The moisture content over the equatorial Indian Ocean (western sector) reduced significantly after assimilation of SSM/I data, where the model has a tendency to enhance moisture. Major rainfall zones during the monsoon season are brought out well in 6 hour forecasts by the model; however, the rainfall amount increased over the Bay of Bengal due to the assimilation of SSM/I data. These features are consistent with the moisture and wind differences between the two assimilation experiments. A quantitative verification of model rainfall in terms of equitable threat scores indicate that the accuracy of rainfall products is higher when SSM/I data are assimilated. It is seen that the general pattern of rainfall tendency in 24 hour forecasts remains the same irrespective of whether the forecast initial conditions are with or without SSM/I data. Examination of a case of monsoon depression showed that assimilation of SSM/I data improved the analysis.

Precipitable water modelling using artificial neural network in Çukurova region

Precipitable water (PW) is an important atmospheric variable for climate system calculation. Local monthly mean PW values were measured by daily radiosonde observations for the time period from 1990 to 2006. Artificial neural network (ANN) method was applied for modeling and prediction of mean precipitable water data in Çukurova region, south of Turkey. We applied Levenberg-Marquardt (LM) learning algorithm and logistic sigmoid transfer function in the network. In order to train our neural network we used data of Adana station, which are assumed to give a general idea about the precipitable water of Çukurova region. Thus, meteorological and geographical data (altitude, temperature, pressure, and humidity) were used in the input layer of the network for Çukurova region. Precipitable water was the output. Correlation coefficient (R(2)) between the predicted and measured values for monthly mean daily sum with LM method values was found to be 94.00% (training), 91.84% (testing), respectively. The findings revealed that the ANN-based prediction technique for estimating PW values is as effective as meteorological radiosonde observations. In addition, the results suggest that ANN method values be used so as to predict the precipitable water.

Impacts of Urban Processes and Urbanization on Summer Precipitation: A Case Study of Heavy Rainfall in Beijing on 1 August 2006

AbstractFinescale simulations (with 500-m grid spacing) using the Weather Research and Forecasting Model (WRF) were used to investigate impacts of urban processes and urbanization on a localized, summer, heavy rainfall in Beijing. Evaluation using radar and gauge data shows that this configuration of WRF with three-dimensional variational data assimilation of local weather and GPS precipitable water data can simulate this event generally well. Additional WRF simulations were conducted to test the sensitivity of simulation of this storm to different urban processes and urban land-use scenarios. The results confirm that the city does play an important role in determining storm movement and rainfall amount. Comparison of cases with and without the presence of the city of Beijing with respect to the approaching storm shows that the urban effect seems to lead to the breaking of the squall line into convective cells over the urban area. The change of precipitation amount depends on the degree of urbanization (i.e., the change over time in the extent of Beijing city). Model results show that an early urbanization prior to 1980 decreases the maximum rainfall, whereas further urbanization in Beijing is conducive to bifurcating the path of rainfall. According to sensitivity results with a single-layer urban canopy model, the thermal transport (sensible and latent heating) induced by the presence of an urban area apparently is more important than associated momentum transport, with latent and sensible heating apparently having equally important roles in the modification of simulated precipitation. Urban surfaces tend to cause the rainfall to be more locally concentrated. High-rise urban cores may bifurcate the path of rainfall as well as increase the area percentage of heavy rainfall.

A comparison of GPS- and NWP-derived PW data over the Korean Peninsula

Precipitable Water (PW) derived from Global Positioning System (GPS) measurements and numerical weather prediction (NWP) model analysis data were compared to further evaluate the efficacy of applying GPS-derived PW to the NWP model. The spatial and temporal variations of GPS-derived PW during a rainfall event were also examined.

Tropical South America–Atlantic Sector Convective Margins and Their Relationship to Low-Level Inflow

Abstract The edges or margins of tropical convective zones are hypothesized to be sensitive to low-level inflow conditions. The present study evaluates where and to what extent convective margin variability is sensitive to low-level inflow variability using observed precipitation and reanalysis wind and total precipitable water data over the tropical South America–Atlantic sector in austral summer. Composite analysis based on an inflow measure defined by projecting low-level monthly-mean atmospheric boundary layer (ABL) or lower free troposphere (LFT) winds onto either mean horizontal precipitation or precipitable water gradients shows widespread contraction of the edges of convection zones in the direction of stronger convection for anomalously strong low-level inflow; such behavior is consistent with enhanced import of relatively dry air along the edges of convection zones. However, the distinction between ABL and LFT winds may be significant regionally, for example, along the Atlantic ITCZ’s northern margin. Back trajectory analysis is employed to estimate source regions of low-level air masses arriving at margin points over time scales (2–4 days) during which low-level air masses are expected to retain some memory of initial moisture conditions while also undergoing diabatic modification. Probability distribution functions of mean precipitation values encountered along trajectories facilitate objective quantification of the frequency with which trajectories approach the margin from drier areas outside the convection zone. While margin points in the ABL are strongly dominated by inflow (i.e., trajectories originating outside of the convection zone), points in the LFT may show inflow, outflow, or mixed inflow–outflow conditions. LFT locations dominated by inflow trajectories generally correspond to regions with composites exhibiting the clearest signatures of LFT wind variability on precipitation.