1. Introduction

Water vapor is the major greenhouse gas in the atmosphere, which plays an important role in almost all the climate change processes. The transports and phase changes of water vapor directly affect the formation of cloud and precipitation, which modulate the hydrological cycle and the energy balance of the earth. Water vapor also has a strong effect on atmospheric chemistry and photochemistry. An accurate knowledge of the distribution of atmospheric water vapor is needed for climate change assessment, weather prediction, and atmospheric chemistry studies [1–3].

Global water vapor vertical profile can be derived from satellite infrared and microwave sounders (i.e., [4–8]). Nevertheless, no single remote sensing technique is able to completely fulfill the needs for numerical weather prediction, chemistry,

and climate studies in terms of vertical resolution, spatial and temporal coverage, and accuracy. Satellite infrared (IR) and microwave sounders have been routinely used for monitoring the temperature and moisture profiles in the mid and lower troposphere since 1980. Launched in 2002, Atmospheric InfraRed Sounder (AIRS) is a high spectral resolution radiometer onboard NASA Aqua satellite. With those more than 2000 high spectrum resolution channels in infrared wavebands, AIRS is able to provide excellent temperature and water vapor retrievals at the midtroposphere level under clear skies. The AIRS measurements, together with more recent high spectrum resolution infrared (IR) measurements from Atmospheric Sounding Interferometer (IASI, 2006–current), and Cross-track Infrared Sounder (CrIS, 2011–current) have maintained continuous observations of tropospheric water vapor since 2002. However, due to the limitation of resolving power in terms of weighting functions and signal to noise ratio of these instruments, accurate estimates of water vapor concentration in the lower troposphere (LT) are still not available. Infrared sounders cannot sense atmospheric profiles below clouds.

FORMOSAT-3/COSMIC (Formosa Satellite Mission #3/Constellation Observing System for Meteorology, Ionosphere & Climate) satellite in 2006 [13, 15–22]. Different numerical methods and various assumptions for bending angle calculation

Global Water Vapor Estimates from Measurements from Active GPS RO Sensors and Passive…

In this chapter, we describe the current developments of global water vapor vertical profile and total precipitable water derived from active GPS RO measurements. We will also demonstrate the potential improvement of global water vapor estimates using combined active GPS RO and passive IR/MW measurements. We introduce the COSMIC temperature and moisture products in Section 2. The combined inversion algorithm is summarized in Section 3. The inversion simulation results are summarized in Section 4. The validation of inversion results from the combined RO-AMSU retrievals using collocated radiosonde observation (RAOBs) is

COSMIC observations distribute relatively uniformly in space and time (see Figure 1). By placing a GPS receiver in LEO, GPS RO technique measures the phase delay of the radio signal from the GPS constellation precisely as the signal traverses the Earth's atmosphere. Being an active limb-sounding measurement, GPS RO technique is capable of retrieving profiles of microwave refractivity at very high vertical resolution [23]. The root mean square (RMS) error was estimated to be less than 1 K based on a detailed theoretical study [23], and this estimate was found to be consistent with numerous cross-validation studies between RO, radiosonde observation (RAOB), and other satellite measurements (e.g., [20, 21, 24–28]). Although RO measurements are not sensitive to clouds, they are very sensitive to the vertical structure of atmospheric density profiles (a function of temperature, pressure, and water vapor profiles). When accurate RO observations, precise positions, and velocities of GPS and LEO satellites are known, accurate atmospheric temperature and moisture profiles can be derived [15, 29]. In a neutral atmosphere,

Typical distribution of COSMIC GPS radio occultation soundings (green dots) over a 24-h period over the

global. Red dots are the distribution of operational radiosonde stations.

shown in Section 5. We conclude this study in Section 6.

2. COSMIC temperature and water vapor retrievals

can be found in [17, 22].

DOI: http://dx.doi.org/10.5772/intechopen.79541

Figure 1.

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While infrared data are limited to clear skies, microwave (MV) sounders can provide all sky data products. There are three main microwave radiometers with sufficient resolution and stability to measure tropospheric water vapor: the Advanced Microwave Sounding Unit-A (AMSU-A) and Advanced Microwave Sounding Unit-B (AMSU-B) on NOAA-15, 16, and 17 (i.e., N15, N16, and N17) satellites, the Microwave Humidity Sounder (MHS) on NOAA-18 (N18) and MetOp-A (Meteorological Operational satellite A) satellites, and Advanced Technology Microwave Sounder (ATMS) on Suomi National Polar orbiting Partnership (SNPP) and the first Joint Polar Satellite System (JPSS-1). These microwave sounders onboard the polar-orbiting satellites have been routinely used by NOAA to generate the tropospheric temperature and moisture profiles for all-sky conditions and hydrological variables (e.g., rainfall, precipitable water, cloud water, ice water, etc.) under cloudy conditions.

Using a one-dimensional variational (1DVAR) scheme, the NOAA Microwave Integrated Retrieval System (MiRS) inversion package is routinely applied to the AMSU/MHS sensors onboard the NOAA-18 (N18), NOAA-19 (N19), and Meteorological Operational Satellite-A (Metop-A) satellites to optimally retrieve temperature, moisture, and surface skin temperature (SST), as well as hydrometer variables within clouds over land and oceans [9, 10]. The MiRS-retrieved parameters have been validated globally using independent measurements [9, 11, 12]. However, studies demonstrated that the MiRS-derived hydrometer parameters within clouds over lands and oceans still contain uncertainty, especially in the lower troposphere [10]. This is partly because there is not enough information from the AMSU/ATMS measurements to completely resolve the hydrometer variables, temperature, and water vapor profiles under clouds.

In addition to the passive infrared and microwave sounder observations, the active global positioning system (GPS) radio occultation (RO) technique can also provide all-weather temperature and moisture profiles. Unlike passive MW and IR sensors, GPS RO is an active remote sensing technique that can provide all-weather, high vertical resolution (from 100 m near the surface to 1.5 km at 40 km) bending angle, and refractivity profiles [13, 14]. With knowledge of the precise positions and velocities of the GPS and low earth orbiting (LEO) satellites, which carry the GPS receivers, a vertical distribution of bending angles at the ray perigee point (the point of the ray path that is closest to Earth) can be derived. From the vertical distribution of the bending angle, we can derive a vertical distribution of the atmospheric bending angle and refractivity, which is a function of atmospheric temperature, moisture, and pressure [13, 14]. The GPS RO data have been intensively used for weather prediction and climate studies since the launch of the

Global Water Vapor Estimates from Measurements from Active GPS RO Sensors and Passive… DOI: http://dx.doi.org/10.5772/intechopen.79541

FORMOSAT-3/COSMIC (Formosa Satellite Mission #3/Constellation Observing System for Meteorology, Ionosphere & Climate) satellite in 2006 [13, 15–22]. Different numerical methods and various assumptions for bending angle calculation can be found in [17, 22].

In this chapter, we describe the current developments of global water vapor vertical profile and total precipitable water derived from active GPS RO measurements. We will also demonstrate the potential improvement of global water vapor estimates using combined active GPS RO and passive IR/MW measurements. We introduce the COSMIC temperature and moisture products in Section 2. The combined inversion algorithm is summarized in Section 3. The inversion simulation results are summarized in Section 4. The validation of inversion results from the combined RO-AMSU retrievals using collocated radiosonde observation (RAOBs) is shown in Section 5. We conclude this study in Section 6.
