Principal Components of Multifrequency Microwave Land Surface Emissivities. Part II: Effects of Previous-Time Precipitation

You, Yalei; Turk, F. Joseph; Haddad, Ziad S.; Li, Li; Liu, Guosheng

doi:10.1175/jhm-d-13-07.1

Cited by 18 publications

(19 citation statements)

References 30 publications

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“…Since Δ H 19 may reflect the precipitation accumulation in the time period of Δ t , it is possible to estimate the precipitation accumulation from Δ H 19. In fact, previous studies estimated the precipitation accumulation (e.g., daily accumulation) from emissivity at low frequencies channels (You et al, ) or from soil moisture (Brocca et al, ). However, further analysis shows that the correlation between Δ H 19 and the precipitation accumulation in the time period of Δ t is not necessarily stronger than that between Δ H 19 and the instantaneous precipitation, because the correlation between Δ H 19 and the precipitation accumulation is dependent on the time interval in which the precipitation accumulation is computed.…”

Section: Resultsmentioning

confidence: 99%

“…Over the ocean where the microwave emissivity is low, the brightness temperature (TB) increase due to the radiometrically warm raindrops is apparent. However, the high surface emissivity over land largely masks the information from liquid water (e.g., Ferraro et al, ; Wang et al, ; Wilheit, ; You et al, ). In addition, the land surface emissivity is highly inhomogeneous, which makes it difficult to physically model the land surface emissivity accurately on the global scale Tian et al ().…”

Section: Introductionmentioning

confidence: 99%

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The Instantaneous Retrieval of Precipitation Over Land by Temporal Variation at 19 GHz

et al. 2018

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The primary signal used in all current passive microwave precipitation retrieval algorithms over land is the depression of the instantaneous brightness temperature (TB) caused by ice scattering. This study presents a new methodology to retrieve instantaneous precipitation rate over land by using TB temporal variation (ΔTB) at 19 GHz, which primarily reflects the surface emissivity variation due to the precipitation impact. As a proof‐of‐concept, we exploit observations from five polar‐orbiting satellites over the Southern Great Plains of the United States. Results show that ΔTB at 19 GHz correlate well with the instantaneous precipitation rate. Further analysis shows that ΔTB at 19 GHz is better correlated with the precipitation rate when multiple satellite observations are used due to the much shorter revisit time for a certain location. The retrieved instantaneous precipitation rate over Southern Great Plains from ΔTB at 19 GHz reasonably agrees with the surface radar observations, with the correlation, the root‐mean‐square error and the bias being 0.49, 2.39 mm/hr, and 6.54%, respectively. Future work seeks to combine the ice scattering signal at high frequencies and this surface emissivity variation signal at low frequencies to achieve an optimal retrieval performance.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Instantaneous Retrieval of Precipitation Over Land by Temporal Variation at 19 GHz

et al. 2018

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“…Then these PCs are used to reconstruct the 10-channel emissivities. More details can be found in Turk et al [2014] and You et al [2014]. This method is denoted as STAT-EOF hereafter.…”

Section: 1002/2015jd023582mentioning

confidence: 99%

An examination of methods for estimating land surface microwave emissivity

et al. 2015

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Land surface emissivity is a critical variable for the passive microwave‐based remote sensing of the land and atmosphere. Driven by the Global Precipitation Measurement mission, we implemented and evaluated a variety of approaches for quantitative estimation of land surface emissivity and its variability, within a well‐defined common framework. These approaches fall into three classes: physical modeling, statistical modeling, and a hybrid of physical and statistical modeling. Every approach is subject to evaluation against retrieved emissivity over a large area in the Southern Great Plains for a period of 2 years. Physical modeling, based on two radiative transfer models coupled to a land surface modeling framework, produced reasonable estimates, with channel‐ and polarization‐dependent errors. Calibration of these models with historical data substantially improved their performance at lower frequencies. The statistical method was tested with five different regression models, and each of them consistently outperformed physical models by about 50%. The best statistical model had an average error of 0.9–2.1%. These statistical models were slightly improved when empirical orthogonal function analysis was incorporated in the emissivity data. The hybrid approach produced errors between the uncalibrated and calibrated physical model errors. In addition to their predictive performance, other aspects of each approach's strengths and weaknesses are discussed.

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“…Current work is ongoing assessing the level of accuracy required for these fields at the algorithm level. Alternatives to forward modeling address MWE more indirectly, and include the identification of "surface-blind" pseudo-channels [7,8], indexing by similarity classes based on monthly MWE climatology [9], empirical approaches to account for recent precipitation [10,11], and hybrid approaches that combine both modeling and statistical approaches [12,13]. As it underpins the similarity class [9] method, a climatological approach to MWE estimation is also evaluated (in which daily estimates are interpolated from monthly means).…”

Section: Introductionmentioning

confidence: 99%

Calibration to Improve Forward Model Simulation of Microwave Emissivity at GPM Frequencies Over the U.S. Southern Great Plains

Harrison

Tian

Peters‐Lidard

et al. 2016

IEEE Trans. Geosci. Remote Sensing

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Better estimation of land surface microwave emissivity promises to improve over-land precipitation retrievals in the GPM era. Forward models of land microwave emissivity are available but have suffered from poor parameter specification and limited testing. Here, forward models are calibrated and the accompanying change in predictive power is evaluated. With inputs (e.g., soil moisture) from the Noah land surface model and applying MODIS LAI data, two microwave emissivity models are tested, the Community Radiative Transfer Model (CRTM) and Community Microwave Emission Model (CMEM). The calibration is conducted with the NASA Land Information System (LIS) parameter estimation subsystem using AMSR-E based emissivity retrievals for the calibration dataset. The extent of agreement between the modeled and retrieved estimates is evaluated using the AMSR-E retrievals for a separate 7-year validation period. Results indicate that calibration can significantly improve the agreement, simulating emissivity with an across-channel average root-mean-square-difference (RMSD) of about 0.013, or about 20% lower than if relying on daily estimates based on climatology. The results also indicate that calibration of the microwave emissivity model alone, as was done in prior studies, results in as much as 12% higher across-channel average RMSD, as compared to joint calibration of the land surface and microwave emissivity models. It remains as future work to assess the extent to which the improvements in emissivity estimation translate into improvements in precipitation retrieval accuracy.

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Principal Components of Multifrequency Microwave Land Surface Emissivities. Part II: Effects of Previous-Time Precipitation

Cited by 18 publications

References 30 publications

The Instantaneous Retrieval of Precipitation Over Land by Temporal Variation at 19 GHz

The Instantaneous Retrieval of Precipitation Over Land by Temporal Variation at 19 GHz

An examination of methods for estimating land surface microwave emissivity

Calibration to Improve Forward Model Simulation of Microwave Emissivity at GPM Frequencies Over the U.S. Southern Great Plains

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