Precipitable Water Data Research Articles

Enhancing Extreme Precipitation Forecasts through Machine Learning Quality Control of Precipitable Water Data from Satellite FengYun-2E: A Comparative Study of Minimum Covariance Determinant and Isolation Forest Methods

Enhancing Extreme Precipitation Forecasts through Machine Learning Quality Control of Precipitable Water Data from Satellite FengYun-2E: A Comparative Study of Minimum Covariance Determinant and Isolation Forest Methods

Evaluation of PWV products derived from satellite-based and radiosonde retrievals for the eastern anatolia observatory (DAG)

In this study, we have presented the results of the precipitable water vapor (PWV) for the Eastern Anatolia Observatory (in Turkish: Doğu Anadolu Gözlemevi, the acronym is DAG) site in Erzurum, Türkiye. The DAG has Türkiye’s largest and the first near infrared (NIR) telescope with a mirror diameter of 4 meters at the altitude of 3170 m. The DAG telescope is going to take the first light in the end of summer 2024. This study is focused on the examining of the precipitable water vapor data for the NIR observations at the DAG. In this context, the NWC SAF Total Precipitable Water (TPW) data obtained by both the satellite based and the radiosonde balloon validated with six radiosonde stations were examined by temporal, vertical and horizontal analyses for the DAG site between June 2019 to December 2020. The results obtained from these analyzes indicate that the mean and median TPW values at the DAG site were approximately 7 mm and the minimum and maximum values were 0.59 mm and 24.12 mm, respectively. The monthly median TPW values at the DAG site varied between approximately 3-10 mm, with a decreasing trend from June to January and an increase in the first seven months of 2020. These results also indicate that the TPW data obtained by its 15 minutes temporal resolution, aligns closely with the radiosonde measurements. Furthermore, the values of PWV at both lower and upper levels of the atmosphere are minimal while the values increase slightly in the middle layer of the atmosphere. As a result, the effective monitoring of the PWV in a site would result in the generation of higher quality astronomical IR observations and be important in terms of the optimum operating cost for an observatory.

Validation of precipitable water vapor estimates from an inexpensive infrared thermometer

Water vapor is a fundamental component of the Earth's atmosphere with a high spatial and temporal variability. This work studies to what extent low-cost infrared thermometers can infer precipitable water (variable commonly used to characterize atmospheric water vapor). In a calibration process, infrared thermometer readings recorded at Badajoz (Spain) during the 2015–2018 period are compared against precipitable water data measured with reference ground-based Global Navigation Satellite Systems (GNSS) in order to obtain conversion factors through regression analyses considering two exponential fits. After this calibration, using the equation of the best fit, thermometer readings for the year 2019 are transformed into precipitable water estimates. A validation analysis in which these estimates are compared with GNSS measurements yields rms differences of 19% and 17% when normal and seasonal calibration had been employed, respectively. These results are similar (or even better) to those obtained with satellite data. In addition, we explore if certain factors, such as solar elevation, precipitable water content, precipitable water measurements used as reference and equations to convert temperature readings into precipitable water estimates, can significantly affect the quality of the estimates. In view of our results, low-cost infrared thermometers could be used to create an extensive and dense network for a better characterization of the spatial and temporal variability of water vapor.

Improvement of GOCI-II Water Vapor Absorption Correction through Fusion with GK-2A/AMI Data

In remote sensing of the ocean color, in particular, in coarse-resolution global model simulations, atmospheric trace gases including water vapor are generally treated as auxiliary data, which create uncertainties in atmospheric correction. The second Korean geostationary satellite mission, Geo-Kompsat 2 (GK-2), is unique in combining visible and infrared observations from the second geostationary ocean color imager (GOCI-II) and the advanced meteorological imager (AMI) over Asia and the Pacific Ocean. In this study, we demonstrate that AMI total precipitable water (TPW) data to allow realistic water vapor absorption correction of GOCI-II color retrievals for the ocean. We assessed the uncertainties of two candidate TPW products for GOCI-II atmospheric correction using atmospheric sounding data, and then analyzed the sensitivity of four ocean-color products (remote sensing reflectance [Rrs], chlorophyll-a concentration [CHL], colored dissolved organic matter [CDOM], and total suspended sediment [TSS]) for GOCI-II water vapor transmittance correction using AMI and global model data. Differences between the TPW sources increased the mean absolute percentage error (MAPE) of Rrs from 2.97% to 6.43% in the blue to green bands, higher than the global climate observing system requirements (<5%) at 412 nm. By contrast, MAPE values of 3.53%, 6.18%, and 7.71% were increased to 6.63%, 13.53%, and 16.14% at high sun and sensor zenith angles for CHL, CDOM, and TSS, respectively. Uncertainty analysis provided similar results, indicating that AMI TPW produced approximately 3-fold lower error rates in ocean-color products than obtained using TPW values from the National Centers for Environmental Prediction. These results imply that AMI TPW can improve the accuracy and ability of GOCI-II ocean-color products to capture diurnal variability.

GPS-Based Multi-Temporal Variation in Precipitable Water over the Territory of Poland

An increase in temperature causes higher evaporation of water from water bodies; consequently, the water content in the atmosphere also increases. The precipitable water (PW), as the water content in the atmospheric air column, is therefore an important parameter to consider when studying climate change. The aim of this study was to analyse multi-annual precipitable water data derived from a dense Global Navigational Satellite Systems (GNSS) network. Twelve years of observations from over a hundred ASG-EUPOS stations were used to estimate changes in precipitation water values over Poland. The data were validated by comparison with the available radio-sounding data. The analysis of the GPS-based PW values showed an upward trend in the PW value of 0.078 mm/year. The spatio-temporal distribution of the mean PW values and their fluctuations over the years were studied and visualised in the form of maps. The results are congruent with the fact that Poland lies on the border of influence of both continental and oceanic climates. Our results are also consistent with other climate research concerning this region.

Improving the accuracy of global precipitation measurement integrated multi-satellite retrievals (GPM IMERG) using atmosphere precipitable water and altitude in climatic regions of Iran

ABSTRACT The global precipitation measurement integrated multi-satellite retrievals (GPM IMERG) provide a new opportunity to estimate precipitation remotely. However, the accuracy of estimations varies in different regions. Therefore, the aim of this study is to improve the IMERG precipitation estimations using two influencing factors including total precipitable water (TPW) and altitude data. The investigations were performed in six rainfall climatic regions of Iran using daily and six-hourly IMERG estimations in years 2015 to 2017. TPW was extracted from a locally developed algorithm for the moderate resolution imaging spectroradiometer (MODIS) measurements. Different models named as M1 to M4 were investigated by considering combinations of the linear and quadratic forms of the IMERG precipitation, TPW, and altitude. The results were evaluated by root mean square error (RMSE), correlation coefficient (r), and Nash-Sutcliffe (NSc). The results showed that the M1 represents the best performance among the proposed models, which uses linear relationship of the IMERG estimations and TPW along with the constant value. The mean values of r, RMSE, and NSc criteria for daily precipitation estimations of IMERG were acquired as 0.46, 5.88, and −2.77 mm, respectively. Those values were improved by M1 to 0.52, 0.18, and 0.27 mm, respectively. Moreover, the evaluation results of IMERG six-hourly precipitation estimations showed r, RMSE, and NSc of 0.65, 11.26, and −33.27 mm. The values of these criteria were improved by M1 to 0.65, 0.07, and 0.32 mm. In general, the results proved the ability of TPW to improve IMERG precipitation estimations in the study area.

Validation of OMI seasonal and spatio-temporal variations in aerosol-cloud interactions over Banizoumbou using AERONET data

Aerosols and cloud interaction remains highly important in the climatic process and its validation with space-based Ozone Monitoring Instrument (OMI) and ground-based observation of Aeronet Robotic Network (AERONET) is therefore essential. OMI and AERONET AODs was logrithmically subset and variations observed in both instruments show spatio-temporal quality of being more noticeable, which could lead to changes in the microphysical scale of clouds and instrumental biases. In this study, we have examined the spatio-temporal OMI AODs variations over Banizoumbou and validated results using AERONET AOD, precipitable water and cloud fraction data from Moderate Resolution Imaging Spectroradiometer (MODIS) from the period between 2014 and 2018. Additionally, estimated the absolute mean bias values of both AODs, correlating the results and performing the mean and standard deviation analysis. To validate instrumental error in retrieving AOD values, we calculated the root mean square error (RMSE) and mean absolute error (MAE) with standard error of measurements and perform 7 day kinematic back trajectories at various initial pressures to estimate zonal and meridional jet winds. Additionally, the seasonal loading of aerosols from the Dust Surface Mass Concentration shows aerosols masking cloud. The results show that winter season has period of good AODs agreement and the absolute bias error and its standard deviation for OMI and AERONET AODs is 0.216 ± 0.08. The correlation results obtained with cloud and precipitable water are 0.51, 0.32 and 0.20, −0.18 which show high monsoon AOD dominance the desert. It could be concluded that instrumental performance has spatio-temporal bias.

Evaluation of NCEP/NCAR Reanalysis Precipitable Water Data Comparing to Radiosonde Observations for Turkey

Precipitable Water (PW) data of NCEP/NCAR Reanalysis Project (NNRP) model is evaluated by comparing to radiosonde data obtained from 8 locations of Turkey for the years between 2015 and 2017. Two methods are utilized to extract NNRP data for the observation locations. In the first method, the nearest NNRP grid point to the radiosonde locations is selected. The second method is the application of bilinear interpolation method on NNRP data to include the weighted effects of corresponding grid locations related with the observation sites. Both NNRP and radiosonde data have 12 h interval for the times 0000 Z and 1200 Z. PW output of NNRP model is compared to observations by means of graphical evaluation of time series, error analyses (Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Root Mean Squared Error (nRMSE)), goodness of fit tests (Cp and PBIAS), and probability density functions (PDF). Error analyses of most of the observation locations indicate that bilinear interpolation method is better than utilizing the nearest grid value data which is not obtained by applying any interpolation technique. Error analyses indicate that nRMSEs of NNRP data for PW analyses are less than 10% for 6 locations of Turkey (Ankara, Diyarbakir, Erzurum, Isparta, Istanbul, and Izmir) if it is assumed that the observations have no errors for the years between 2015 and 2017. nRMSEs of the other 2 coastal locations (Adana and Samsun) are the same as 13.8% and this may indicate that local moisture sources of these locations are greater than mesoscale moisture fields, since NNRP data may not capture local effects well due to its spatial resolution. Comparisons of probability density functions (PDF) of these data sets show that NNRP model may not be successful in capturing extreme values.

Biases and Abrupt Shifts of Monthly Precipitable Water from Terra MODIS

Monthly atmospheric precipitable water (PW) from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Terra satellite was assessed over land at 60°S–60°N. MODIS provides two PW products by using infrared (IR) and near-IR (NIR) algorithms, respectively. An assessment was performed for both MODIS PW data from 2000 to 2014, comparing them with the measurements at international stations of the global positioning systems and with a reanalysis to detect abrupt changes through monthly variations. It is noted that MODIS IR systematically underestimated PW in over 75% of stations, and that PW estimation declines with time. MODIS NIR significantly overestimated PW for tropical land and experienced two abrupt shifts. These data defects result in large spurious decreasing trends in MODIS IR and increasing trends in MODIS NIR. The two MODIS PW products are currently not suitable for a climatic-trend analysis, highlighting the need for data reprocessing and calibration.

Application of Cumulative Distribution Functions to Compositing Precipitable Water with Low Earth Orbit Satellite Data

Precipitable water (PW) is a leading indicator of severe weather, such as heavy rainfall. The National Meteorological Satellite Center receives observation data directly from Low Earth Orbit (LEO) satellites, including NOAA-18, 19, MetOp-A, B, and Suomi-NPP, and generates level 2 products in near real time for the East Asia region. In this study, PW data were retrieved from the abovementioned sources and composited to determine meteorologically significant PW fields from all available data sources considering the boundary layer (BL), middle layer (ML), high layer (HL), and total column. Cumulative distribution function (CDF) matching was performed for PW retrieved from the five satellites within specific time windows in order to integrate them and minimize their differences. The PW composite data were matched to reference PW CDF data from the Unified Model (UM) Regional Data Assimilation Prediction System (RDAPS) analysis data, which are updated according to selected time windows. PW data were composited for 03:00–09:00, 09:00–15:00, 15:00–21:00, and 21:00–03:00 UTC time windows. PW data composited during the summer and winter seasons in 2015, 2016, and 2017 were then validated. The results show improvements in composite Total Precipitable Water and BL PW from Suomi-NPP of approximately 15% and 40% (RMSE), respectively.

Role of Moisture Transport and Recycling in Characterizing Droughts: Perspectives from Two Recent U.S. Droughts and the CFSv2 System

Abstract We investigate the role of moisture transport and recycling in characterizing two recent drought events in Texas (2011) and the Upper Midwest (2012) by analyzing the precipitation, evapotranspiration, precipitable water, and soil moisture data from the Climate Forecast System version 2 (CFSv2) analysis. Next, we evaluate the CFSv2 forecasts in terms of their ability to capture different drought signals as reflected in the analysis data. Precipitation from both sources is partitioned into recycled and advected components using a moisture accounting–based precipitation recycling model. All four variables reflected drought signals through their anomalously low values, while precipitation and evapotranspiration had the strongest signals. Drought in Texas was dominated by the differences in moisture transport, whereas in the Upper Midwest, the absence of strong precipitation-generating mechanisms was a crucial factor. Reduced advection from the tropical and midlatitude Atlantic contributed to the drought in Texas. The Upper Midwest experienced reduced contributions from recycling, terrestrial sources, the midlatitude Pacific, and the tropical Atlantic. In both cases, long-range moisture transport from oceanic sources was reduced during the corresponding drought years. June and August in Texas and July and August in the Upper Midwest were the driest months, and in both cases, drought was alleviated by the end of August. Moisture from terrestrial sources most likely contributed to alleviating drought intensity in such conditions, even with negative anomalies. The forecasts showed noticeable differences as compared to the analysis for multiple variables in both regions, which could be attributed to several factors as discussed in this paper.

GPS RO precipitable water as a possible proxy for rainfall distribution over tropical South America: qualitative analysis

A principal component analysis (PCA) is applied to radio occultation vertical profiles of water vapor and rainfall data over tropical South America (SA). The low-level flow over this region is climatologically dominated by the Atlantic anticyclone and the trade winds. This results in an inflow of oceanic moist air to the continent from the east, which is forced to lift over the Andes range at the west. Precipitable water data (PW) for the period June 2006–February 2014 derived from the COSMIC/FORMOSAT-3 mission and monthly accumulated rainfall data (R) obtained from Tropical Rainfall Measuring Mission (TRMM) are compared. PCA in modes S and T is performed to find qualitative similarities and differences between both variables. This study shows that specific humidity profiles are able to reproduce a significant amount of the precipitation patterns over tropical SA, as they explain most of the variability during the June 2006–February 2014 period. Therefore, PW may be used as a proxy for rainfall data over the region.

Construction and Uncertainty Estimation of a Satellite‐Derived Total Precipitable Water Data Record Over the World's Oceans

AbstractMeasurements of total precipitable water (TPW) from 11 satellite‐borne microwave imaging radiometers are intercalibrated and merged into a single gridded monthly data set starting in January 1988 and continuing to the present. The resulting data set shows a global mean, ocean‐only trend in TPW of 0.436 kg/m2 per decade (1.49% per decade), and a trend in the deep tropics (20°S–20°N) of 0.629 kg/m2 per decade (1.503% per decade). The uncertainty in the merged TPW results is analyzed on multiple time and distance scales using a Monte Carlo approach. This analysis results in an uncertainty ensemble that can be used to evaluate the effects of construction uncertainty on any subsequent application of the data set. We compare the merged data set with measurements of TPW from ground‐based Global Navigation Satellite System sensors on small islands. These comparisons show no systematic long‐term drifts in the differences, and the observed differences were largely explained by our uncertainty analysis. As an example of the use of the uncertainty ensemble, we evaluate the increase in TPW over the Gulf of Mexico during summer months over 1988–2017.

Optimal Interpolation of Precipitable Water Using Low Earth Orbit and Numerical Weather Prediction Data

The National Meteorological Satellite Center/Korean Meteorological Administration (NMSC/KMA) receives data directly from low Earth orbit (LEO) satellites (including NOAA-18,19; MetOp-A,B; and Suomi-NPP), and generates Level 2 products (e.g., temperature and humidity profile) in near real time. Total precipitable water (TPW) and layer precipitable water (LPW) are also generated using the retrieved humidity profiles. Today, forecasters need meteorologically-significant data fields composited from all available data sources, not multiple maps of observations from individual sources. Hence, TPW and LPW are reproduced using the optimal interpolation (OI) method with numerical weather prediction (NWP) data, in order to generate composite precipitable water (PW) products. In the OI procedure, PW data retrieved from the LEO satellites serve as observation data, while PW data from NWP serve as background data. Error covariances are estimated using a new approach, which considers correlations between observation errors to describe the characteristics of the errors better. Both background and observation error covariance matrices may have non-zero off-diagonal components. The composite PW products are validated using radiosonde data. The validation results for optimally-interpolated LPW (OI LPW) are much better than those for optimally-interpolated TPW (OI TPW). Generally, the OI LPW validation results are better than those for observation and background data; OI LPW data are ~5–10% more accurate than background data. Optimally-interpolated PW (OI PW) fields are applied to the correction of NWP forecast fields and the prediction of severe weather.

Precipitable water characteristics during the 2013 Colorado flood using ground-based GPS measurements

Abstract. During 9–16 September 2013, the Front Range region of Colorado experienced heavy rainfall that resulted in severe flooding. Precipitation totals for the event exceeded 450 mm, damages to public and private properties were estimated to be over USD 2 billion, and nine lives were lost. This study analyzes the characteristics of precipitable water (PW) surrounding the event using 10 years of high-resolution GPS PW data in Boulder, Colorado, which was located within the region of maximum rainfall. PW in Boulder is dominated by seasonal variability with an average summertime maximum of 36 mm. In 2013, the seasonal PW maximum extended into early September and the September monthly mean PW exceeded the 99th percentile of climatology with a value 25 % higher than the 40-year climatology. Prior to the flood, around 18:00 UTC on 8 September, PW rapidly increased from 22 to 32 mm and remained around 30 mm for the entire event as a result of the nearly saturated atmosphere. The frequency distribution of September PW for Boulder is typically normal, but in 2013 the distribution was bimodal due to a combination of above-average PW values from 1 to 15 September and much drier conditions from 16 to 30 September. The above-normal, near-saturation PW values during the flood were the result of large-scale moisture transport into Colorado from the Tropical Eastern Pacific and the Gulf of Mexico. This moisture transport was the product of a stagnating cutoff low over the southwestern United States working in conjunction with an anticyclone located over the southeastern United States. A blocking ridge located over the Canadian Rocky Mountains kept both of the synoptic features in place over the course of several days, which helped to provide continuous moisture to the storm, thus enhancing the accumulated precipitation totals.

Assimilation of Radar Radial Velocity and Reflectivity, Satellite Cloud Water Path, and Total Precipitable Water for Convective-Scale NWP in OSSEs

AbstractIn this study, the ensemble of three-dimensional variational data assimilation (En3DVar) method for convective-scale weather is adopted and evaluated using an idealized supercell storm simulated by the Weather Research and Forecasting (WRF) Model. Synthetic radar radial velocity, reflectivity, satellite-derived cloud water path (CWP), and total precipitable water (TPW) data are produced from the simulated supercell storm and then these data are assimilated into another WRF Model run that starts with no convection. Two types of experiments are performed. The first assimilates radar and satellite CWP data using a perfect storm environment. The second assimilates additional TPW data using a storm environment with dry bias. The first set of experiments indicates that incorporating CWP and radar data into the assimilation leads to a much faster initiation of supercell storms than found using radar data alone. Assimilating CWP data primarily improves the analyses of nonprecipitating hydrometeor variables. The results from the second set of experiments demonstrate the critical importance of the storm environment. When using the biased storm environment, assimilation of CWP and radar data enhances the analyses, but the forecast skill rapidly decreases over the subsequent 1-h forecast. Further experiments show that assimilating the TPW data has a large impact on storm environment that is essential to the accuracy of the storm forecasts. In general, the combination of radar data and satellite data within the En3DVar results in better analyses and forecasts than when only radar data are used, especially for an imperfect storm environment.

Assimilation Impact of Different GPS Analysis Methods on Precipitation Forecast: A Heavy Rainfall Case Study of Kani City, Gifu Prefecture on July 15, 2010

In this study, we examined variations in predicted precipitable water produced from different Global Positioning System (GPS) zenith delay methods, and assessed the corresponding difference in predicted rainfall after assimilating the obtained precipitable water data. Precipitable water data estimated from the GPS and three-dimensional horizontal wind velocity field derived from the X-band dual polarimetric radar were assimilated in CReSS and rainfall forecast experiments were conducted for the heavy rainfall system in Kani City, Gifu Prefecture on July 15, 2010. In the GPS analysis, a method to simultaneously estimate coordinates and zenith delay, i.e., the simultaneous estimation method, and a method to successively estimate coordinates and zenith delay, i.e., the successive estimation method, were used to estimate precipitable water. The differences generated from using predicted orbit data provided in pseudo-real time from the International GNSS (Global Navigation Satellite System) Service for geodynamics (IGS) versus precise orbit data released after a 10-day delay were examined. The change in precipitable water due to varying the analysis methods was larger than that due to the type of satellite orbit information. In the rainfall forecast experiments, those using the successive estimation method results had a better precision than those using the simultaneous estimation method results. Both methods that included data assimilation had higher rainfall forecast precisions than the forecast precision without precipitable water assimilation. Water vapor obtained from GPS analysis is accepted as important in rainfall forecasting, but the present study showed additional improvements can be attained from incorporating a zenith delay analysis method.

Evaluation of Total Precipitable Water from CRCM4 using the NVAP-MEaSUREs Dataset and ERA-Interim Reanalysis Data

The fourth-generation Canadian Regional Climate Model’s (CRCM4) precipitable water is evaluated and compared with observational data and ERA-Interim reanalysis data over five Canadian basins with simulations driven by ERA-Interim (two) and global climate models (two). Considering the 22 years of data available in the observations, we analyze precipitable water’s behaviour through its annual cycle, its daily distribution, and its annual daily maxima. For the simulations driven by reanalyses, differences in annual daily maximum values and their correlations with observations are examined. In general, the values for precipitable water simulated by CRCM4 are similar to those observed, and the model reproduces both the interannual and inter-basin variabilities. The simulation at 15 km resolution produces higher extreme values than simulations performed at 45 km resolution and higher than the observations taken at coarser resolution (1°), without much influence on the mean behaviour. Some underestimation is found with the simulation driven by the Canadian Centre for Climate Modelling and Analysis Model, version 3, a sign of a cold and dry bias, whereas the run driven by the European Centre Hamburg Model, version 5, is much closer to the observations, pointing to the importance of closely considering the regional–global model combination. Overall, CRCM4's ability to reproduce the major characteristics of observed precipitable water makes it a possible tool for providing precipitable water data that could serve as a basis for probable maximum precipitation and probable maximum flood studies at the basin scale.

Global Water Vapor Trend from 1988 to 2011 and Its Diurnal Asymmetry Based on GPS, Radiosonde, and Microwave Satellite Measurements

Abstract This study analyzes trends in precipitable water (PW) over land and ocean from 1988 to 2011, the PW–surface temperature Ts relationship, and their diurnal asymmetry using homogenized radiosonde data, microwave satellite observations, and ground-based global positioning system (GPS) measurements. It is found that positive PW trends predominate over the globe, with larger magnitudes over ocean than over land. The PW trend is correlated with surface warming spatially over ocean with a pattern correlation coefficient of 0.51. The PW percentage increase rate normalized by Ts expressed as is larger and closer to the rate implied by the Clausius–Clapeyron (C–C) equation over ocean than over land. The 2-hourly GPS PW data show that the PW trend from 1995 to 2011 is larger at night than during daytime. Nighttime PW monthly anomalies correlate positively and significantly with nighttime minimum temperature Tmin at all stations, but this is not true for daytime PW and maximum temperature Tmax. The ratio of relative PW changes with Tmin () is larger and closer to the C–C equation’s implied value of ~7% K−1 than . This suggests that the relationship between PW and Ts at night is a better indicator of the water vapor feedback than that during daytime, when clouds and other factors also influence Ts.

Simulation of High-Resolution Precipitable Water Data by a Stochastic Model with a Random Trigger

We use a stochastic differential equation (SDE) model with a random precipitation trigger for mass balance to simulate the 20 s temporal resolution column precipitable water vapor (PWV) data during the tropical warm pool international cloud experiment (TWP-ICE) period of January 20 to February 15, 2006 at Darwin, Australia. The trigger is determined by an exponential cumulative distribution function, the time step size in the SDE simulation, and a random precipitation indicator uniformly distributed over [0, 1]. Compared with the observed data, the simulations have similar means, extremes, skewness, kurtosis, and overall shapes of probability distribution, and are temporally well synchronized for increasing and decreasing, but have about 20% lower standard deviation. Based on a 1000-day run, the correlations between the model data and the observations in TWP-ICE period were computed in a moving time window of 25 days and show quasi-periodic variations between (−0.675, 0.697). This shows that the results are robust for the stochastic model simulation of the observed PWV data, whose fractal dimension is 1.9, while the dimension of the simulated data is also about 1.9. This agreement and numerous sensitivity experiments form a test on the feasibility of using an SDE model to simulate precipitation processes in more complex climate models.