scholarly journals Time‐Lag Correlation Between Passive Microwave Measurements and Surface Precipitation and Its Impact on Precipitation Retrieval Evaluation

2019 ◽  
Vol 46 (14) ◽  
pp. 8415-8423 ◽  
Author(s):  
Yalei You ◽  
Huan Meng ◽  
Jun Dong ◽  
Scott Rudlosky
Keyword(s):  
Time Lag ◽  
Passive Microwave ◽  
Microwave Measurements ◽  
Surface Precipitation ◽  
Time Lag Correlation

Algorithms for the retrieval of rainfall from passive microwave measurements

1994 ◽  
Vol 11 (1-4) ◽  
pp. 163-194 ◽  
Author(s):  
T. Wilheit ◽  
R. Adler ◽  
S. Avery ◽  
E. Barrett ◽  
P. Bauer ◽  
...  
Keyword(s):  
Passive Microwave ◽  
Microwave Measurements

Vertical precipitation profiles estimated by satellite-based passive microwave retrievals

2021 ◽  
Author(s):  
Nobuyuki Utsumi ◽  
F. Joseph Turk ◽  
Ziad. S. Haddad ◽  
Pierre-Emmanuel Kirstetter ◽  
Hyungjun Kim
Keyword(s):  
Water Content ◽  
Passive Microwave ◽  
Precipitation Rate ◽  
Vertical Profiles ◽  
High Latitudes ◽  
Surface Precipitation ◽  
Profile Information ◽  
Precipitation Estimates ◽  
Condensed Water ◽  
Global Precipitation

<p>Passive microwave (MW) observation from low Earth-orbiting satellites is one of the major sources of information for global precipitation monitoring. Although various precipitation retrieval techniques based on passive MW observation have been developed, most of them focus on estimating precipitation rate at near surface height. Vertical profile information of precipitation is meaningful for process-based understanding of precipitation systems. Also, a previous study found that the use of the vertical precipitation profile information can improve sub-hourly surface precipitation estimates (Utsumi et al., 2019).</p><p>This study investigates the precipitation vertical profiles estimated by two passive MW algorithms, i.e., the Emissivity Principal Components (EPC) algorithm developed by authors (Turk et al., 2018; Utsumi et al., 2021) and the Goddard Profiling Algorithm (GPROF). The vertical profiles of condensed water content estimated by the two passive MW algorithms for the Global Precipitation Measurement Microwave Imager (GMI) observations are validation with the GMI + Dual-frequency Precipitation Radar combined algorithm (CMB) for June 2014 – May 2015. The condensed water content profiles estimated by the passive MW algorithms show biases in their magnitude (i.e., EPC underestimates the magnitude by 20 – 50% in the middle-to-high latitudes; GPROF overestimates the magnitude by 20 – 50% in the middle-to-high latitudes and more than 50% overestimation in the tropics). On the other hand, the shapes of the profiles are reproduced well by the passive MW algorithms. The relationship between the estimation performances of surface precipitation rate and vertical profiles are also investigated. It is shown that the error in the profile magnitude shows a clear positive relationship with the surface precipitation error. The estimation performance of the profile shapes also shows connection with the surface precipitation error. This result indicates that physically reasonable connections between the surface precipitation estimate and its associated profiles are achieved to some extent by the passive MW algorithms. This also implies that properly constraining physical parameters of the precipitation profiles would lead to the improvements of the surface precipitation estimates.</p><p>References</p><p>Utsumi, N., Kim, H., Turk, F. J., & Haddad, Ziad. S. (2019). Improving Satellite-Based Subhourly Surface Rain Estimates Using Vertical Rain Profile Information. Journal of Hydrometeorology, 20(5), 1015–1026.</p><p>Turk, F. J., Haddad, Z. S., Kirstetter, P.-E., You, Y., & Ringerud, S. (2018). An observationally based method for stratifying a priori passive microwave observations in a Bayesian-based precipitation retrieval framework. Quarterly Journal of the Royal Meteorological Society, 144(S1), 145–164.</p><p>Utsumi, N., Turk, F. J., Haddad, Z. S., Kirstetter, P.-E., & Kim, H. (2021). Evaluation of Precipitation Vertical Profiles Estimated by GPM-Era Satellite-Based Passive Microwave Retrievals. Journal of Hydrometeorology, 22(1), 95–112.</p>

Multi-Stage Inversion Method to Retrieve Soil Moisture from Passive Microwave Measurements over the Mackenzie River Basin

2013 ◽  
Vol 12 (3) ◽  
pp. vzj2012.0134 ◽  
Author(s):  
Naira Chaouch ◽  
Robert Leconte ◽  
Ramata Magagi ◽  
Marouane Temimi ◽  
Reza Khanbilvardi
Keyword(s):  
Soil Moisture ◽  
River Basin ◽  
Passive Microwave ◽  
Inversion Method ◽  
Microwave Measurements ◽  
Multi Stage ◽  
Mackenzie River Basin ◽  
Mackenzie River

Passive Microwave Measurements Of Sea Surface Roughness During The Saxon Experiment

2005 ◽  
Author(s):  
G.D. Sandlin ◽  
L.A. Rose ◽  
G.L. Geernaert ◽  
J.P. Hollinger ◽  
F.A. Hansen
Keyword(s):  
Surface Roughness ◽  
Passive Microwave ◽  
Sea Surface ◽  
Microwave Measurements ◽  
Sea Surface Roughness

scholarly journals Passive microwave measurements of tundra and taiga snow covers in Alaska, U.S.A.

1993 ◽  
Vol 17 ◽  
pp. 125-130 ◽  
Author(s):  
Matthew Sturm ◽  
Thomas C. Grenfell ◽  
Donald K. Perovich
Keyword(s):  
Crystal Structure ◽  
Grain Size ◽  
Snow Depth ◽  
Marked Reduction ◽  
Basal Layer ◽  
Passive Microwave ◽  
Microwave Measurements ◽  
Effective Emissivity ◽  
Maximum Reduction ◽  
Microwave Emissivity

The microwave emissivity of two snow covers was measured in Alaska in March, 1990. Observations were made on taiga snow near Fairbanks that was 0.83 m thick with a 0.55 m thick basal layer of depth hoar. Other measurements were made on the tundra snow cover at Imnaviat Creek north of the Brooks Range which was 0.27 to 0.64 m thick and consisted of two or more wind slabs overlying a depth hoar layer 0.14 to 0.26 m thick. Density, crystal structure, and grain size were similar in tundra and taiga depth hoar layers.Emissivity was measured at 18.7 and 37 GHz using radiometers mounted on a 1.5 m tall bipod. Measurements were made on undisturbed snow, and then several snow layers were removed and additional measurements were made. This sequence was repeated until all snow had been removed. Effective emissivity values for the full snow depth ranged from 0.6 (37 GHz, H-pol) to 0.95 (18.7 GHz, V-pol) and were similar for both taiga and tundra snow covers. For both snow covers, there was a marked reduction in the effective emissivity (eeff) from that of the underlying ground with a maximum reduction of about 30%. All of the reduction was found to occur within the depth hoar layer. Maximum reduction in eeffcould be caused by a depth hoar layer 0.3 m thick. Overlying wind slab or new snow were nearly “invisible”, increasing the effective emissivity only by a small amount due to self-emittance. Thus, it was difficult to distinguish the two different snow covers on the basis of their emissivity, since both contained 0.3 m of depth hoar or more.

scholarly journals IMERG V06: Changes to the Morphing Algorithm

2019 ◽  
Vol 36 (12) ◽  
pp. 2471-2482 ◽  
Author(s):  
Jackson Tan ◽  
George J. Huffman ◽  
David T. Bolvin ◽  
Eric J. Nelkin
Keyword(s):  
Large Scale ◽  
Numerical Models ◽  
Precipitable Water ◽  
Passive Microwave ◽  
Precipitation Field ◽  
Motion Vectors ◽  
Surface Precipitation ◽  
General Improvement ◽  
Lagrangian Interpolation ◽  
Cloud Tops

AbstractAs the U.S. Science Team’s globally gridded precipitation product from the NASA–JAXA Global Precipitation Measurement (GPM) mission, the Integrated Multi-Satellite Retrievals for GPM (IMERG) estimates the surface precipitation rates at 0.1° every half hour using spaceborne sensors for various scientific and societal applications. One key component of IMERG is the morphing algorithm, which uses motion vectors to perform quasi-Lagrangian interpolation to fill in gaps in the passive microwave precipitation field using motion vectors. Up to IMERG V05, the motion vectors were derived from the large-scale motions of infrared observations of cloud tops. This study details the changes introduced in IMERG V06 to derive motion vectors from large-scale motions of selected atmospheric variables in numerical models, which allow IMERG estimates to be extended from the 60°N–60°S latitude band to the entire globe. Evaluation against both instantaneous passive microwave retrievals and ground measurements demonstrates the general improvement in the precipitation field of the new approach. Most of the model variables tested exhibited similar performance, but total precipitable water vapor was chosen as the source of the motion vectors for IMERG V06 due to its competitive performance and global completeness. Continuing assessments will provide further insights into possible refinements of this revised morphing scheme in future versions of IMERG.

Sign in / Sign up

Export Citation Format

Share Document