Estimation of Downwelling Surface Longwave Radiation under Heavy Dust Aerosol Sky

Abstract

Abstract:The variation of aerosols, especially dust aerosol, in time and space plays an important role in climate forcing studies. Aerosols can effectively reduce land surface longwave emission and re-emit energy at a colder temperature, which makes it difficult to estimate downwelling surface longwave radiation (DSLR) with satellite data. Using the latest atmospheric radiative transfer code (MODTRAN 5.0), we have simulated the outgoing longwave radiation (OLR) and DSLR under different land surface types and atmospheric profile conditions. The results show that dust aerosol has an obvious "warming" effect to longwave radiation compared with other aerosols; that aerosol longwave radiative forcing (ALRF) increased with the increasing of aerosol optical depth (AOD); and that the atmospheric water vapor content (WVC) is critical to the understanding of ALRF. A method is proposed to improve the accuracy of DSLR estimation from satellite data for the skies under heavy dust aerosols. The AOD and atmospheric WVC under cloud-free conditions with a relatively simple satellite-based radiation model yielding the high accurate DSLR under heavy dust aerosol are used explicitly as model input to reduce the effects of dust aerosol on the estimation of DSLR. Validations of the proposed model with satellites data and field measurements show that it can estimate the DSLR accurately under heavy dust aerosol skies. The root mean square errors (RMSEs) are 20.4 W/m 2 and 24.2 W/m 2 for Terra and Aqua satellites, respectively, at the Yingke site, and the biases are 2.7 W/m 2 and 9.6 W/m 2 , respectively. For the Arvaikheer site, the RMSEs are 23.2 W/m 2 and 19.8 W/m 2 for Terra and Aqua, respectively, and the biases are 7.8 W/m 2 and 10.5 W/m 2 , respectively. The proposed method is especially applicable to acquire relatively high accurate DSLR under heavy dust aerosol using MODIS data with available WVC and AOD data.

Influence of water vapor and aerosols on downward longwave radiation in the high mountain region of Musala peak, Bulgaria

“…They also concluded that dust has a positive radiative effect in the longwave spectrum, both at the Earth's surface and at the top of the atmosphere. Similar conclusions are drawn in the paper by Wang et al (2017), who also studied dust, concluding that the greater its amount in the atmosphere is, the greater its positive radiative effect in the longwave spectrum is. The importance of water vapor in such type of effect assessment is emphasized.…”

Using the Himawari-8 AHI Multi-Channel to Improve the Calculation Accuracy of Outgoing Longwave Radiation at the Top of the Atmosphere

“…During the southern hemisphere summer, the bias and RMSE of OLR 4ch and CERES OLR for the area around the Australian desert were 0.37 Wm −2 and 6.52 Wm −2 , which was a decrease 7.46 Wm −2 and 3.73 Wm −2 compared to the difference in the single-channel OLR 1ch and CERES OLR. In regions like the Australian desert, which are dry and have a high surface temperature, changes in OLR are sensitive to absorption gas in the atmosphere [10,[41][42][43][44]; therefore, the OLR calculation algorithm must be built using channel information that can properly reflect these changes. (a,b,c,d), the difference between these two OLR values (e,f,g,h), and the distribution of the differences among OLR 1ch , OLR 2ch , and OLR 4ch (i,j,k,l).…”

“…Water vapor is already known as a very important factor in reducing OLR [10,39]. CO 2 also reduces OLR as its concentrations increase continually [40], and there are also studies on OLR reductions caused by O 3 and aerosols [41][42][43][44]. However, these must use channels that are sensitive to absorption gases because there are regional patterns and long-term changes in this factor and increases in the concentration of absorption gases in the atmosphere reduce OLR [15].…”

Estimation of Downwelling Surface Longwave Radiation with the Combination of Parameterization and Artificial Neural Network from Remotely Sensed Data for Cloudy Sky Conditions

“…In clear-sky conditions, many researchers have used satellite-based radiances measured at TOA to develop different statistical regression methods to estimate DSLR [25][26][27]. Some scholars have modified Tang and Li's [25] method to make it suitable for different situations [28,29], showing that the algorithm is relatively mature. Tang and Li's [25] algorithm is expressed as follows:…”