Microphysical cross validation of spaceborne radar and ground polarimetric radar

Chandrasekar, V.; Bolen, S.; Gorgucci, E.

doi:10.1109/tgrs.2003.817186

Cited by 15 publications

(7 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Because of their similar electrical properties, the KuPR-only products are produced by the algorithm which is similar to the TRMM PR algorithm , the attenuation correction of which is based on a hybrid method that provides a smooth transition between the Hitschfeld-Bordan (Hitschfeld and Bordan, 1954) method and the surface reference technique (Meneghini et al, 2000). Considering no systematic differences between the TRMM PR attenuation-corrected reflectivity and ground-based polarimetric radar reflectivity measurements (Bolen and Chandrasekar, 2000;Chandrasekar et al, 2003), there is no reason to believe that the near-surface reflectivity of the KuPR-only products is biased systematically toward under-or overcorrection. The Ku-only rain type classification module (Awaka et al, 2016) is similar to the TRMM PR rain type classification algorithm 2A23 ).…”

Section: Methodsmentioning

confidence: 99%

Vertical gradient of stratiform radar reflectivity below the bright band from the Tropics to the extratropical latitudes seen by GPM

Kobayashi

Shige

Yamamoto

2018

Quart J Royal Meteoro Soc

View full text Add to dashboard Cite

This study examined the vertical gradient of radar reflectivity below the detected bright band in stratiform regions from the Tropics to the extratropical latitudes using data from the Ku‐band (13.6 GHz) precipitation radar on board the Global Precipitation Measurement (GPM) Core Observatory. Stratiform precipitation profiles with reflectivity decreasing (increasing) from the melting level toward the surface occur frequently in the tropical oceans (mid‐ and high‐latitude oceans). High fractions of downward increasing stratiform pixels are found over the North Pacific Ocean throughout the year and over East Asia except for winter. In contrast, the North American continent and the adjacent North Atlantic Ocean are characterized by low fractions of downward‐increasing pixels during summer. The difference is consistent with the dominant type of convection over East Asia (warm‐type clouds) and over the North American continent (cold‐type clouds). Even in the tropical oceans such as the Atlantic and eastern Pacific intertropical convergence zones, there are some areas with moderate fractions of downward‐increasing stratiform pixels where the warm rain process dominates. The downward reduction of reflectivity in the stratiform region of MCSs is obviously due to evaporation, which is a function of lower‐tropospheric relative humidity. The downward increase of reflectivity in stratiform regions over the midlatitude oceans appears the result of raindrop growth. This is achieved via the collection of cloud droplets while falling through low‐level clouds produced by large‐scale vertical motion in the lower troposphere due to large‐scale convergence associated with synoptic‐scale systems.

show abstract

Section: Methodsmentioning

confidence: 99%

Vertical gradient of stratiform radar reflectivity below the bright band from the Tropics to the extratropical latitudes seen by GPM

Kobayashi

Shige

Yamamoto

2018

Quart J Royal Meteoro Soc

View full text Add to dashboard Cite

show abstract

“…Chandrasekar et al (2003b) studied the DSD profiles from two precipitation regions, coincident with both TRMM PR overpass and ground radar (GR) measurements, and showed that both the GR and TRMM PR estimated the D o profiles to be fairly linear in the rain region; and the GR showed that the log(N w ) profile could easily be fit by a line. Chandrasekar et al (2003a), using data from the Texas and Florida Underflights (TEFLUN)-B experiment and the TRMM Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA), showed that the profiles of both D o and log(N w ) could be approximated by linear functions. Bringi et al (2004) showed DSD profiles from a convective cell beginning at the early growth stage and developing into a more mature phase, resulting in an intense microburst.…”

Section: A Backgroundmentioning

confidence: 99%

A GPM Dual-Frequency Retrieval Algorithm: DSD Profile-Optimization Method

Rose

Chandrasekar

2006

Journal of Atmospheric and Oceanic Technology

Self Cite

View full text Add to dashboard Cite

A new dual-frequency precipitation radar (DPR) will be included on the Global Precipitation Measurement (GPM) core satellite, which will succeed the highly successful Tropical Rainfall Measuring Mission (TRMM) satellite launched in 1997. New dual-frequency drop size distribution (DSD) and rain-rate estimation algorithms are being developed to take advantage of the enhanced capabilities of the DPR. It has been shown that the backward-iteration algorithm can be embedded within a single-loop (SL) feedback model. However, the SL model is unable to correctly estimate DSD profiles for a significant portion of global median volume diameter D o and normalized DSD intercept parameter N w combinations in rain because of a multiple-value solution space. For the remaining D o , N w pairs, another retrieval method is necessary. This paper proposes a supplementary profile-optimization technique to find those DSD profiles in the rain region that the SL model cannot correctly determine. The optimization method is based on a model that both D o and log(N w ) are linear vertical profiles, and that the profiles can be found using an optimization technique from the input reflectivity profiles. Using those assumptions, the optimization method finds the top and bottom D o , log(N w ) values such that a cost function related to the input-measured reflectivity is minimized. A random-restart method is used to generate random top-and-bottom DSD seed values for each optimization cycle. Example cases are shown to demonstrate the performance with and without error in the input reflectivity profiles. Limitations of the method are discussed, including its performance when the input reflectivity profiles are based on nonlinear DSD profiles and values of shape factor different than the algorithm assumed value.

show abstract

“…Previous efforts to generate a model for simulating higher-frequency radar observations from lower-frequency ground data typically relied on fitting linear and nonlinear functions to scattering simulation results as discussed in previous sections and Chandrasekar et al (2006) and Chandrasekar and Khajonrat (2009). The models can work very well as long as the variance of data resulting from Mie scattering effects is relatively small or highly correlated to another variable such as Z dr .…”

Section: The Emran-rbf Model For Rain and Hailmentioning

confidence: 99%

“…By simulating the observations of the same particle volume at different frequencies and pointing angles the relationship between the two radar systems can be modeled. Models have been developed in the past for small hydrometeors and large raindrops to a certain extent Le et al 2009;Chandrasekar and Khajonrat 2009), although errors can be significant when the particle size is close enough to the radar wavelength that results in Mie scattering. For this reason, and because of the relatively low probability of observing hailstorms from space radar, models to simulate hail measurements from real data have not been developed.…”

Section: Introductionmentioning

confidence: 99%

Simulating Radar Observations of Precipitation at Higher Frequencies from Lower-Frequency Polarimetric Measurements

Fritz

Chandrasekar

2012

Journal of Atmospheric and Oceanic Technology

Self Cite

View full text Add to dashboard Cite

The translation of radar data from one frequency to another based on electromagnetic absorption and scattering models has been used to convert S-band measurements to higher frequencies, such as those within the X or Ku bands, in order to create realistic simulations. As the target frequency of simulations for weather radar increase to X band and above, the size of large raindrops and hail either approach or exceed the radar wavelength, resulting in a radar cross section that is no longer in the linear Rayleigh region. The Mie solution to scattering models is then required. With the advent of dual-polarization systems, a more complete characterization of the effect of precipitation on radar is possible in order to improve model effectiveness. However, typical curve-or surface-fitting methods still have limitations as particle size increases. A more robust solution is presented here in the form of a neural network that incorporates the nonlinear relationship among various polarimetric observation variables and the radar wavelength and look angle. Thus, high-frequency observations of a convective storm containing hail can be simulated using polarimetric ground radar measurements. Adequate polarimetric data from hail storms at high frequencies do not exist, however, so network outputs for this case can only be compared to theoretical observations. An application of the simulation procedure to characterize the effect of precipitation on spaceborne synthetic aperture radar (SAR) using ground observations is presented.

show abstract

Microphysical cross validation of spaceborne radar and ground polarimetric radar

Cited by 15 publications

References 23 publications

Vertical gradient of stratiform radar reflectivity below the bright band from the Tropics to the extratropical latitudes seen by GPM

Vertical gradient of stratiform radar reflectivity below the bright band from the Tropics to the extratropical latitudes seen by GPM

A GPM Dual-Frequency Retrieval Algorithm: DSD Profile-Optimization Method

Simulating Radar Observations of Precipitation at Higher Frequencies from Lower-Frequency Polarimetric Measurements

Contact Info

Product

Resources

About