4.1 Temperature and water vapor profiles derived from the combined RO-AIRS and RO-MW measurements in the troposphere and the boundary layer

To illustrate how the collocated RO data benefit the AIRS retrievals, we conduct a multiple variable regression simulation study to inverse RO and AIRS measurements simultaneously to obtain the temperature and water vapor profiles. The SARTA [47] is used to simulate the AIRS radiances. The simulated AIRS brightness temperatures (BTs) and RO refractivity measurements are computed by applying the NOAA-88b temperature and moisture profiles to AIRS and RO refractivity forward operators plus the known AIRS instrument noises and RO refractivity measurement noises, respectively.

inversion of RO and Atmospheric Infrared Sounder (AIRS) data [34, 48]. To illustrate how the increased amounts of RO data benefit the AMSU retrievals, we conduct a multiple variable regression simulation study using RO and MW measurements that are inversed simultaneously to retrieve the temperature and water vapor profiles. Figure 6 shows the temperature and moisture RMSEs from the multiple variable regression simulation study of the GPS RO, AMSU, and the combined GPS RO and AMSU retrievals. The simulated AMSU BTs and RO refractivity measurements are computed by applying the NOAA-88b temperature and moisture profiles to AMSU and RO refractivity forward operators plus the known AMSU instrument noises and RO refractivity measurement noises, respectively.

The RMSE of retrieval results for temperature (left panel) and water vapor mixing ratio (right panel) for

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

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

AMSU, GPS RO, and ASMU combined with GPS RO measurements over the globe.

It is shown in the right panel of Figure 6 that because RO data are very sensitive

to water vapor variation, the RMSEs for AMSU water vapor mixing ratio at the surface decreases from 1.3 g/kg (for AMSU only retrievals) to 0.4 g/kg when both GPS RO and AMSU data are used. The left panel of Figure 6 shows that AMSU temperature measurements tremendously improve the GPS RO temperature retrieval when both GPS RO and AMSU data are used. These retrieval results demonstrate that the nadir viewing AMSU and limb-viewing GPS observations act to constrain the individual solutions; therefore providing much improved water vapor retrievals, particularly in the middle and lower troposphere. This is also demonstrated that by adding the RO refractivity operator in the AIRS inversion package described in [48], we are able to constrain the temperature and moisture profiles from the RO-AIRS observations and obtain the improved atmospheric

thermal structure, which was not possible for individual sensors.

RO measurements

97

Figure 6.

4.2 Improving the temperature and water vapor retrievals in the upper troposphere and stratosphere using combined AIRS, AMSU, and GPS

as well as the combined AIRS, AMSU, and GPS RO data. The multi-variable

In this section, we used GPS RO data to constrain the AIRS and AMSU temperature retrievals serving to improve moisture retrievals in the upper troposphere and lower stratosphere (UT/LS). In the upper troposphere, GPS RO refractivity is very sensitive to temperature but less sensitive to moisture. It is demonstrated by Ho et al. [34] that GPS RO refractivity can resolve temperatures greater than 1 K around 200 hPa but it can only sense about 15% of water vapor variation. Figure 7 shows the temperature and moisture retrieval RMSE for AIRS, AMSU, and GPS RO

The temperature and moisture root mean square errors (RMSEs) for RO, AIRS, and the combined RO and AIRS retrievals are plotted in Figure 5. It is demonstrated in Figure 5 that the combined AIRS and RO observations act to constrain the individual solutions. The significantly improved water vapor RMSE is found in both the middle and lower troposphere. The RMSEs of water vapor mixing ratio for AIRS and RO improved from 1.5 and 1.0 g/kg at surface, respectively, to 0.5 g/kg for the GPS RO combined AIRS retrievals. Since GPS refractivity is less sensitive to temperature in the troposphere, only small temperature RMSE improvements are found.

The synergy of using RO observations with microwave observations has been demonstrated in numerous studies, including the comparison of Microwave Sounding Unit (MSU)/AMSU climate records with RO data in the upper troposphere [19], evaluating the accuracy of Special Sensor Microwave Imager (SSMI) water vapor retrievals [44–46, 49], and developing methods for concurrent

Figure 5.

The RMSE of retrieval results for temperature (left panel) and water vapor mixing ratio (right panel) for AIRS, GPS, and AIRS combined with GPS measurements.

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

Figure 6.

package—SARTA [47]) is served as an AIRS forward operator. The configurations of background covariance matrix, error covariance matrix, and a priori used in the concurrent RO-AIRS retrieval are detailed in [48] and are not further

4. Simulation inversion of the combined measurements in the

4.1 Temperature and water vapor profiles derived from the combined RO-AIRS and RO-MW measurements in the troposphere and the

To illustrate how the collocated RO data benefit the AIRS retrievals, we conduct a multiple variable regression simulation study to inverse RO and AIRS measurements simultaneously to obtain the temperature and water vapor profiles. The SARTA [47] is used to simulate the AIRS radiances. The simulated AIRS brightness temperatures (BTs) and RO refractivity measurements are computed by applying the NOAA-88b temperature and moisture profiles to AIRS and RO refractivity forward operators plus the known AIRS instrument noises and RO refractivity

The temperature and moisture root mean square errors (RMSEs) for RO, AIRS, and the combined RO and AIRS retrievals are plotted in Figure 5. It is demonstrated in Figure 5 that the combined AIRS and RO observations act to constrain the individual solutions. The significantly improved water vapor RMSE is found in both the middle and lower troposphere. The RMSEs of water vapor mixing ratio for AIRS and RO improved from 1.5 and 1.0 g/kg at surface, respectively, to 0.5 g/kg for the GPS RO combined AIRS retrievals. Since GPS refractivity is less sensitive to temperature in the troposphere, only small temperature RMSE improvements

The synergy of using RO observations with microwave observations has been

demonstrated in numerous studies, including the comparison of Microwave Sounding Unit (MSU)/AMSU climate records with RO data in the upper troposphere [19], evaluating the accuracy of Special Sensor Microwave Imager (SSMI) water vapor retrievals [44–46, 49], and developing methods for concurrent

The RMSE of retrieval results for temperature (left panel) and water vapor mixing ratio (right panel) for

AIRS, GPS, and AIRS combined with GPS measurements.

troposphere and lower stratosphere

descripted here.

Green Chemistry Applications

boundary layer

are found.

Figure 5.

96

measurement noises, respectively.

The RMSE of retrieval results for temperature (left panel) and water vapor mixing ratio (right panel) for AMSU, GPS RO, and ASMU combined with GPS RO measurements over the globe.

inversion of RO and Atmospheric Infrared Sounder (AIRS) data [34, 48]. To illustrate how the increased amounts of RO data benefit the AMSU retrievals, we conduct a multiple variable regression simulation study using RO and MW measurements that are inversed simultaneously to retrieve the temperature and water vapor profiles. Figure 6 shows the temperature and moisture RMSEs from the multiple variable regression simulation study of the GPS RO, AMSU, and the combined GPS RO and AMSU retrievals. The simulated AMSU BTs and RO refractivity measurements are computed by applying the NOAA-88b temperature and moisture profiles to AMSU and RO refractivity forward operators plus the known AMSU instrument noises and RO refractivity measurement noises, respectively.

It is shown in the right panel of Figure 6 that because RO data are very sensitive to water vapor variation, the RMSEs for AMSU water vapor mixing ratio at the surface decreases from 1.3 g/kg (for AMSU only retrievals) to 0.4 g/kg when both GPS RO and AMSU data are used. The left panel of Figure 6 shows that AMSU temperature measurements tremendously improve the GPS RO temperature retrieval when both GPS RO and AMSU data are used. These retrieval results demonstrate that the nadir viewing AMSU and limb-viewing GPS observations act to constrain the individual solutions; therefore providing much improved water vapor retrievals, particularly in the middle and lower troposphere. This is also demonstrated that by adding the RO refractivity operator in the AIRS inversion package described in [48], we are able to constrain the temperature and moisture profiles from the RO-AIRS observations and obtain the improved atmospheric thermal structure, which was not possible for individual sensors.
