Comparison of precipitable water over Hawaii using AVHRR-based split-window techniques, GPS and radiosondes

In this study, global positioning system (GPS)-derived precipitable water vapor (PWV) and microwave radiometer (MWR)-measured integrated water vapor (IWV) were compared and their characteristics were analyzed. Comparing those two quantities for two years from August 2009, we found that GPS PWV estimates were larger than MWR IWV. The average differ ence over the entire test period was 1.1 mm and the standard deviation was 1.2 mm. When the discrepancies between GPS PWV and MWR IWV were analyzed depending on season, the average difference was 0.7 mm and 1.9 mm in the winter and summer months, respectively. Thus, the average difference was about 2.5 times larger in summer than that in winter. However, MWR IWV measurements in the winter months were over-estimated than those in the summer months as the water vapor content got larger. The results of the diurnal analysis showed that MWR IWV was underestimated in the daytime, showing a difference of 0.8 mm. In the early morning hours, MWR IWV has a tendency to be over-estimated, with a difference of 1.3 mm with respect to GPS PWV.

Section: Introductionmentioning

confidence: 99%

Comparison of the Characteristics of Precipitable Water Vapor Measured by Global Positioning System and Microwave Radiometer

Sohn¹,

Park²,

Won³

et al. 2012

“…It has been also known that the time resolution is mostly limited to 2 times per day and the accuracys of temperature and relative humidity are only about 0.2 °C and 3.5 %, respectively, in general (Elliot & Gaffen 1991). Due to these, the PWV values derived from the radiosonde data inherently include error in the range of 5-10% (Motell et al 2002).…”

Section: Introductionmentioning

confidence: 99%

Analysis on Characteristics of Radiosonde Bias Using GPS Precipitable Water Vapor

Park¹,

Baek²,

Cho³

2010

As an observation instrument of the longest record of tropospheric water vapor, radiosonde data provide upper-air pressure (geopotential height), temperature, humidity and wind. However, the data have some well-known elements related to inaccuracy. In this article, radiosonde precipitable water vapor (PWV) at Sokcho observatory was compared with global positioning system (GPS) PWV during each summertime of year 2007 and 2008 and the biases were calculated. As a result, the mean bias showed negative values regardless of the rainfall occurrence. In addition, on the basis of GPS PWV, the maximum root mean square error (RMSE) was 5.67 mm over the radiosonde PWV.

“…Among these, radiosonde is the oldest instrument for the tropospheric water vapor observation and it generates the vertical data of the atmospheric pressure (geopotential height), air temperature, relative humidity and wind, 2-4 times per day. Although radiosonde is one of the key measurement tools in measuring the atmospheric water vapor from the past, the temporal resolution is limited to two time per day in most cases and many studies took note of the problems of the radiosonde water vapor observation such as the inaccuracy and bias (Elliott & Gaffen 1991, Lorenc et al 1996, Motell et al 2002.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Radiosonde Daily Bias by Comparing Precipitable Water Vapor Obtained from Global Positioning System and Radiosonde

Park

Cho²

2010

and analyzed the radiosonde bias between the day and the night. In the scatter diagram of the daytime and nighttime radiosonde PWV data and the GPS PWV data, dry bias was found in the daytime radiosonde observation as known in the previous study. In addition, for all the rainfall events, the tendency that the wet bias of the radiosonde PWV increased as the GPS PWV decreased and the dry bias of the radiosonde PWV increased as the GPS PWV increased was significantly less distinctive in nighttime than in daytime. The quantitative analysis of the bias and error of the radiosonde PWV data showed that the mean bias decreased in the second year, regardless of nighttime or daytime rainfall, and the non-rainfall root mean square error (RMSE) was similar to that of the previous studies, while the rainfall RMSE was larger to a certain extent.