Integration of Satellite Soil Moisture and Rainfall Observations over the Italian Territory

Abstract

State-of-the-art rainfall products obtained by satellites are often the only way of measuring rainfall in remote areas of the world. However, it is well known that they may fail in properly reproducing the amount of precipitation reaching the ground, which is of paramount importance for hydrological applications. To address this issue, an integration between satellite rainfall and soil moisture SM products is proposed here by using an algorithm, SM2RAIN, which estimates rainfall from SM observations. A nudging scheme is used for integrating SM-derived and state-of-the-art rainfall products. Two satellite rainfall products are considered: H05 provided by EUMESAT and the real-time (3B42-RT) TMPA product provided by NASA. The rainfall dataset obtained through SM2RAIN, SM2R ASC , considers SM retrievals from the Advanced Scatterometer (ASCAT). The rainfall datasets are compared with quality-checked daily rainfall observations throughout the Italian territory in the period 2010-13. In the validation period 2012-13, the integrated products show improved performances in terms of correlation with an increase in median values, for 5-day rainfall accumulations, of 26% (18%) when SM2R ASC is integrated with the H05 (3B42-RT) product. Also, the median root-mean-square error of the integrated products is reduced by 18% and 17% with respect to H05 and 3B42-RT, respectively. The integration of the products is found to improve the threat score for medium-high rainfall accumulations. Since SM2R ASC , H05, and 3B42-RT datasets are provided in near-real time, their integration might provide more reliable rainfall products for operational applications, for example, for flood and landslide early warning systems.

Correction of real-time satellite precipitation with satellite soil moisture observations

“…A sequential assimilation approach was applied where AMSR-E C-band TB measurements were used to estimate a simple multiplicative factor to the precipitation estimates in order to minimize the difference between observed (AMSR-E) and simulated TBs in terms of root mean square error (RMSE). The results show improvements over those found in Pellarin et al (2009). Specifically, the Pellarin et al (2013) study shows that the proposed methodology produces an improvement of the RMSE at daily, decadal and monthly timescales and at the three locations.…”

“…Many other studies have utilized satellite microwave brightness temperatures or soil moisture retrievals to constrain satellite precipitation estimates (Pellarin et al, 2008), estimate precipitation (e.g., Brocca et al, 2013) or improve precipitation estimates through assimilation (Crow et al, 2009(Crow et al, , 2011. Here, we review their approaches and findings in light of the results of this study, and compare our results with some of these studies to gain insight into their robustness and consistency.…”

“…A promising avenue for rainfall correction is through the assimilation of satellite-based surface soil moisture into a water balance model (Pan and Wood, 2006). Over land, the physical relationship between variations in soil water storage and rainfall accumulation contain complementary information that can be exploited for the mutual benefit of both types of products Crow et al, 2009). Unlike instantaneous rain rate, satellite surface soil moisture retrievals utilize low-frequency microwave signals and possess some memory reflecting antecedent rainfall amounts.…”

“…In particular, Brocca et al (2014) estimated daily rainfall on a global scale based on satellite SM products by inverting the soil water balance equation. Crow et al (2003Crow et al ( , 2009Crow et al ( , 2011 corrected space-borne rainfall retrievals by assimilating remotely sensed surface soil moisture retrievals into an Antecedent Precipitation Index (API) based soil water balance model using a Kalman filter (Kalman, 1960). However, these studies focused on multi-day aggregation periods and a space aggregated correction at 1 • resolution for the corrected precipitation totals.…”

“…For instance, the RMS mean error decreases from 7.7 to 3.5 mm day −1 at the daily timescale in Niger and from 18.3 to 7.7 mm day −1 at the decadal timescale in Mali. Crow et al (2003Crow et al ( , 2009Crow et al ( , 2011 demonstrated the effectiveness of the assimilation of remotely sensed microwave brightness temperatures or retrieved soil moisture in esti- Brocca et al (2013) estimated precipitation by inverting the water budget equation such that precipitation could be estimated from changes in soil moisture. The inverted equation was calibrated using in situ, 4-day averaged observations at two sites in Spain and Italy.…”