Spatio-Temporal Variability of the Precipitable Water Vapor over Peru through MODIS and ERA-Interim Time Series

Abstract

Precipitable water vapor (PWV) is a meteorological variable that influences the main processes that occur in the atmosphere. It is not a homogeneous variable, but varies both temporally and spatially according to local conditions. This study analyzes the spatial and temporal variability of the PWV in Peru using MODIS satellite data (MOD05/MYD05 products) during the period 2000 to 2017. MODIS-derived PWV values were complemented with ERA-Interim reanalysis data to take the study period back to 1979. PWV values extracted from MODIS and ERA-Interim were compared against in situ values obtained from five radiosonde stations between the years of 2003 and 2016 (non-continuous data). The study was performed over nine sub-regions of the Peruvian territory: coastal, highland, and jungle sub-regions, which in turn were classified into northern, central and southern regions. The analysis of spatial variability was performed using monthly semivariograms and influencing parameters such as sill and range, whereas the temporal variation was examined by time series of monthly, seasonal, and multi-annual means. The Mann-Kendall test was also applied to determine the presence of trends. The spatial analysis evidenced the heterogeneity of the PWV over the study region, and in most of the sub-regions there was directional variability during the austral summer and austral winter, with the Northeast (NE) and East (E) directions having the greatest spatial variability. The omnidirectional analysis of the sill and range showed that there was a high spatial variability of PWV mainly over the northern and southern jungle, even exceeding the limit area of these sub-regions. The temporal analysis shows that this variability occurs more in the north and center of the jungle and in the north coast, where the content of PWV is higher in relation to other regions, while the central and southern highlands have the lowest values. In addition, the trend test determines that there is a slight increase in PWV for the coast and jungle regions of Peru. Validation analysis using the radiosonde data showed a similar performance of both datasets (MODIS and ERA), with better results for the case of the MODIS product (RMSE < 0.6 cm and R 2 = 0.71). pattern, especially over oceans, where the PWV amount is higher over the Equator and lower over the Poles. However, over continental lands there is a significant influence of the orography, with the lowest values over high altitude sites and locations far away from the sea, and the highest values over low elevation sites and sites near the coast. The PWV also follows a seasonal cycle, with higher values in summer and lower values in winter.The knowledge of the spatial and temporal variations of PWV are extremely important to produce realistic outputs from climate models [1]. This is especially important for Peru because of the number of microclimates that characterize this country. However, as far as we know, these parameters have been poorly characterized for Peru.The main objective of this work is to provide a...

Response of Meiyu Process Considering the Temporal and Spatial Characteristics of GNSS PWV

“…The basic idea of the massive air parcel tracking analysis method is: (1) to calculate the spatial location and physical properties of the air parcels based on HYSPLIT and simulation schemes; (2) to determine the position of the air parcel at a certain time before reaching the study area, and count the number and physical properties of the air parcels in the grid; (3) to analyze the spatial characteristics of the number and physical properties of the air parcels in the grid to understand the distribution of water vapor in the area.…”

“…Its content can be represented by PWV, which, despite its low content, is the most active component in the hydrological cycle [2]. The spatiotemporal variability of water vapor is an important driving force for many meteorological disasters [3]. Therefore, in-depth exploration of the multiscale spatiotemporal characteristics of water vapor is of significant importance for climate change research, short-term weather forecasting, and disaster weather warnings.As early as 1998, Davis J. L. conducted a comprehensive analysis of the spatiotemporal structure of water vapor in the Global Positioning System (GPS) and established Integrated Precipitable Water Vapor (IPWV) based on temporal and spatial scales, finding a close relationship between the spatiotemporal variability of IPWV and atmospheric processes [4].…”

Variability and Trend in Integrated Water Vapour from ERA-Interim and IGRA2 Observations over Peninsular Malaysia

“…The IWV products from the ERA reanalysis are based on various water vapour measurements, which include clear-sky radiance observations from geostationary and polar-orbiting sounders, and the images of remote sensing satellites like the Special Sensor Microwave Imager (SSM/I), Total Ozone Mapping Spectrophotometer (TOMS), TIROS Operational Vertical Sounder (TOVS), and High Resolution Infrared Radiation Sounder. Detailed documentation is found in [30]. ERA has been used in several climate and trend analysis studies [15,17].…”

“…In the current study, therefore, both radiosonde data from the Integrated Global Radiosonde Archive (IGRA) version 2 and the ERA-Interim reanalysis product from ECMWF [30] for the period 1988-2018 are used to investigate variability and trends in IWV over Peninsular Malaysia. ERA is one of the most advanced global atmospheric reanalysis records, which has been revised and improved upon over the years and used elsewhere to evaluate both variability and trends in IWV [5,7,19,30]. Moreover, the data compare well with radiosonde observations [31].…”

Consistency Evaluation of Precipitable Water Vapor Derived From ERA5, ERA‐Interim, GNSS, and Radiosondes Over China

“…Besides, Mengistu Tsidu et al () analyzed the PWV variation based on the ERA‐Interim and GPS in Ethiopia in the case of the complex topography, and the results indicate that the root‐mean‐square (RMS) of differences between them is about 3–6 mm. Ccoica‐López et al () evaluated the accuracy of the PWV derived from the ERA‐Interim with respect to the radiosondes in Peru, and the RMS is indicated to be about 9 mm. In addition to the retrieval of the PWV, the pressure interpolated from the reanalysis data sets can be used in the computation of the PWV with GNSS observations for improved accuracy due to a lack of meteorological sensors for recording the pressure at some GNSS stations (Chen et al, ; Jade & Vijayan, ; Jiang et al, ).…”