The Impact of Assimilating GPS Precipitable Water Vapor in Convective-Permitting WRF-ARW on North American Monsoon Precipitation Forecasts over Northwest Mexico

2021 ◽  
Author(s):  
Christoforus Bayu Risanto ◽  
Christopher L. Castro ◽  
Avelino F. Arellano ◽  
James M. Moker ◽  
David K. Adams
Keyword(s):  
Water Vapor ◽  
Data Assimilation ◽  
North American ◽  
Precipitable Water ◽  
Precipitable Water Vapor ◽  
North American Monsoon ◽  
Convective Systems ◽  
Northwest Mexico ◽  
Dewpoint Temperature ◽  
The Impact

AbstractWe assess the impact of GPS precipitable water vapor (GPS-PWV) data assimilation (DA) on short-range North American monsoon (NAM) precipitation forecasts, across 38 days with weak synoptic forcing, during the NAM GPS Hydrometeorological Network field campaign in 2017 over northwest Mexico. Utilizing an ensemble-based data assimilation technique, the GPS-PWV data retrieved from 18 observation sites are assimilated every hour for 12 hours into a 30-member ensemble convective-permitting (2.5 km) Advanced Research version of the Weather Research and Forecasting (WRF-ARW) model. As the assimilation of the GPS-PWV improves the initial condition of WRF by reducing the root mean square error and bias of PWV across 1200-1800 UTC, this also leads to an improvement in capturing nocturnal convection of mesoscale convective systems (MCSs; after 0300 UTC) and to an increase by 0.1 mm h-1 in subsequent precipitation during the 0300-0600 UTC period relative to no assimilation of the GPS-PWV (NODA) over the area with relatively more observation sites. This response is consistent with observed precipitation from the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement Final Precipitation product. Moreover, compared to the NODA, we find that the GPS-PWV DA decreases cloud top temperature, increases most unstable convective available energy and surface dewpoint temperature, and thus creates a more favorable condition for convective organization in the region.

scholarly journals The North American Monsoon GPS Transect Experiment 2013

2016 ◽  
Vol 97 (11) ◽  
pp. 2103-2115 ◽  
Author(s):  
Yolande L. Serra ◽  
David K. Adams ◽  
Carlos Minjarez-Sosa ◽  
James M. Moker ◽  
Avelino F. Arellano ◽  
...  
Keyword(s):  
Water Vapor ◽  
North American ◽  
Water Cycle ◽  
Precipitable Water ◽  
Annual Rainfall ◽  
North American Monsoon ◽  
Atmospheric Sciences ◽  
The North ◽  
Northwestern Mexico ◽  
The Impact

Abstract 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.

National Weather Service Forecasters Use GPS Precipitable Water Vapor for Enhanced Situational Awareness during the Southern California Summer Monsoon

2015 ◽  
Vol 96 (11) ◽  
pp. 1867-1877 ◽  
Author(s):  
Angelyn W. Moore ◽  
Ivory J. Small ◽  
Seth I. Gutman ◽  
Yehuda Bock ◽  
John L. Dumas ◽  
...  
Keyword(s):  
Water Vapor ◽  
North American ◽  
Southern California ◽  
Situational Awareness ◽  
Precipitable Water ◽  
Flash Floods ◽  
Precipitable Water Vapor ◽  
North American Monsoon ◽  
National Weather Service ◽  
Weather Service

Abstract During the North American Monsoon, low-to-midlevel moisture is transported in surges from the Gulf of California and Eastern Pacific Ocean into Mexico and the American Southwest. As rising levels of precipitable water interact with the mountainous terrain, severe thunderstorms can develop, resulting in flash floods that threaten life and property. The rapid evolution of these storms, coupled with the relative lack of upper-air and surface weather observations in the region, make them difficult to predict and monitor, and guidance from numerical weather prediction models can vary greatly under these conditions. Precipitable water vapor (PW) estimates derived from continuously operating ground-based GPS receivers have been available for some time from NOAA’s GPS-Met program, but these observations have been of limited utility to operational forecasters in part due to poor spatial resolution. Under a NASA Advanced Information Systems Technology project, 37 real-time stations were added to NOAA’s GPS-Met analysis providing 30-min PW estimates, reducing station spacing from approximately 150 km to 30 km in Southern California. An 18–22 July 2013 North American Monsoon event provided an opportunity to evaluate the utility of the additional upper-air moisture observations to enhance National Weather Service (NWS) forecaster situational awareness during the rapidly developing event. NWS forecasters used these additional data to detect rapid moisture increases at intervals between the available 1–6-h model updates and approximately twice-daily radiosonde observations, and these contributed tangibly to the issuance of timely flood watches and warnings in advance of flash floods, debris flows, and related road closures.

scholarly journals On the Diurnal Cycle of GPS-Derived Precipitable Water Vapor over Sumatra

2019 ◽  
Vol 76 (11) ◽  
pp. 3529-3552
Author(s):  
Giuseppe Torri ◽  
David K. Adams ◽  
Huiqun Wang ◽  
Zhiming Kuang
Keyword(s):  
Water Vapor ◽  
Diurnal Cycle ◽  
Wrf Model ◽  
Precipitable Water ◽  
Active Phase ◽  
Precipitable Water Vapor ◽  
Tectonic Activity ◽  
Western Coast ◽  
The Impact ◽  
Spectrometer Data

Abstract 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.

scholarly journals Evaluating Forecast Skills of Moisture from Convective-Permitting WRF-ARW Model during 2017 North American Monsoon Season

Atmosphere ◽  
2019 ◽  
Vol 10 (11) ◽  
pp. 694 ◽  
Author(s):  
Christoforus Bayu Risanto ◽  
Christopher L. Castro ◽  
James M. Moker ◽  
Avelino F. Arellano ◽  
David K. Adams ◽  
...  
Keyword(s):  
North American ◽  
Monsoon Season ◽  
Precipitable Water ◽  
Weather Forecast ◽  
Moisture Flux ◽  
Rain Gauge ◽  
North American Monsoon ◽  
Convective Systems ◽  
The North ◽  
Northwestern Mexico

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.

scholarly journals Assimilation of GNSS PWV with NCAR-RTFDDA to Improve Prediction of a Landfall Typhoon

2022 ◽  
Vol 14 (1) ◽  
pp. 178
Author(s):  
Haishen Wang ◽  
Yubao Liu ◽  
Yuewei Liu ◽  
Yunchang Cao ◽  
Hong Liang ◽  
...  
Keyword(s):  
Water Vapor ◽  
Data Assimilation ◽  
Precipitable Water ◽  
Satellite System ◽  
Navigation Satellite ◽  
System A ◽  
Spiral Rainbands ◽  
Global Navigation Satellite ◽  
Four Dimensional Data Assimilation ◽  
The Impact

Precipitable water vapor (PWV) retrieved from ground-based global navigation satellite system (GNSS) stations acquisition signal of a navigation satellite system provides high spatial and temporal resolution atmospheric water vapor. In this paper, an observation-nudging-based real-time four-dimensional data assimilation (RTFDDA) approach was used to assimilate the PWV estimated from GNSS observation into the WRF (Weather Research and Forecasting) modeling system. A landfall typhoon, “Mangkhut”, is chosen to evaluate the impact of GNSS PWV data assimilation on its track, intensity, and precipitation prediction. The results show that RTFDDA can assimilate GNSS PWV data into WRF to improve the water vapor distribution associated with the typhoon. Assimilating the GNSS PWV improved the typhoon track and intensity prediction when and after the typhoon made landfall, correcting a 5–10 hPa overestimation (too deep) of the central pressure of the typhoon at landfall. It also improved the occurrence and the intensity of the major typhoon spiral rainbands.

scholarly journals Comparisons of Performance Using Data Assimilation and Data Fusion Approaches in Acquiring Precipitable Water Vapor: A Case Study of a Western United States of America Area

Water ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 2943
Author(s):  
Zhaohui Xiong ◽  
Jizhang Sang ◽  
Xiaogong Sun ◽  
Bao Zhang ◽  
Junyu Li
Keyword(s):  
United States ◽  
Water Vapor ◽  
Data Assimilation ◽  
United States Of America ◽  
Weather Prediction ◽  
Wrf Model ◽  
Precipitable Water ◽  
The United States ◽  
Satellite System ◽  
Precipitable Water Vapor

There are two main types of methods available to obtain precipitable water vapor (PWV) with high accuracy. One is to assimilate observations into a numerical weather prediction (NWP) model, for example, the Weather Research and Forecasting (WRF) model, to improve the accuracy of meteorological parameters, and then obtain the PWV with improved accuracy. The other is the direct fusion of multi-source PWV products. Regarding the two approaches, we conduct a comparison experiment on the West Coast of the United States of America with the data from May 2018, in which the WRF data assimilation (DA) system is used to assimilate the Global Navigation Satellite System (GNSS) PWV, while the method by Zhang et al. to fuse the GNSS PWV, ERA5 PWV and MODIS (moderate-resolution imaging spectroradiometer) PWV. As a result, four groups of PWV products are generated: the assimilated GNSS PWV, the unassimilated GNSS PWV, PWV from the fusion of the GNSS PWV and ECWMF (European Centre for Medium-Range Weather Forecasts) ERA5 (ECWMF Reanalysis 5) PWV, and PWV from the fusion of the GNSS PWV, ERA5 PWV and MODIS PWV. Experiments show that the data assimilation based on the WRF model (WRFDA) and adopted fusion method can generate PWV products with similar accuracy (1.47 mm vs. 1.52 mm). Assimilating the GNSS PWV into the WRF model slightly improves the accuracy of the inverted PWV by 0.18 mm. The fusion of the MODIS PWV, GNSS PWV and ERA5 PWV results in a higher accuracy than the fusion of GNSS PWV and ERA5 PWV by a margin of 0.35 mm. In addition, the inland canyon topography appears to have an influence on the inversion accuracy of both the methods.

scholarly journals Establishment and Assessment of a New GNSS Precipitable Water Vapor Interpolation Scheme Based on the GPT2w Model

2019 ◽  
Vol 11 (9) ◽  
pp. 1127 ◽  
Author(s):  
Fei Yang ◽  
Jiming Guo ◽  
Xiaolin Meng ◽  
Junbo Shi ◽  
Lv Zhou
Keyword(s):  
Water Vapor ◽  
Precipitable Water ◽  
Precipitable Water Vapor ◽  
Reference Station ◽  
Observation Data ◽  
Interpolation Scheme ◽  
Grid Data ◽  
Skill Scores ◽  
Gpt2w Model ◽  
The Impact

With the development of Global Navigation Satellite System (GNSS) reference station networks that provide rich data sources containing atmospheric information, the precipitable water vapor (PWV) retrieved from GNSS remote sensing has become one of the most important bodies of data in many meteorological departments. GNSS stations are distributed in the form of scatters, generally, these separations range from a few kilometers to tens of kilometers. Therefore, the spatial resolution of GNSS-PWV can restrict some applications such as interferometric synthetic aperture radar (InSAR) atmospheric calibration and regional atmospheric water vapor analysis, which inevitably require the spatial interpolation of GNSS-PWV. This paper explored a PWV interpolation scheme based on the GPT2w model, which requires no meteorological data at an interpolation station and no regression analysis of the observation data. The PWV interpolation experiment was conducted in Hong Kong by different interpolation schemes, which differed in whether the impact of elevation was considered and whether the GPT2w model was added. In this paper, we adopted three skill scores, i.e., compound relative error (CRE), mean absolute error (MAE), and root mean square error (RMSE), and two approaches, i.e., station cross-validation and grid data validation, for our comparison. Numerical results showed that the interpolation schemes adding the GPT2w model could greatly improve the PWV interpolation accuracy when compared to the traditional schemes, especially at interpolation points away from the elevation range of reference stations. Moreover, this paper analyzed the PWV interpolation results under different weather conditions, at different locations, and on different days.

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