A Climatology of Disdrometer Measurements of Rainfall in Finland over Five Years with Implications for Global Radar Observations

To improve the understanding of high-latitude rain microphysics and its implications for the remote sensing of rainfall by ground-based and spaceborne radars, raindrop size measurements have been analyzed that were collected over five years with a Joss–Waldvogel disdrometer located in Järvenpää, Finland. The analysis shows that the regional climate is characterized by light rain and small drop size with narrow size distributions and that the mutual relations of drop size distribution parameters differ from those reported at lower latitudes. Radar parameters computed from the distributions demonstrate that the high latitudes are a challenging target for weather radar observations, particularly those employing polarimetric and dual-frequency techniques. Nevertheless, the findings imply that polarimetric ground radars can produce reliable “ground truth” estimates for space observations and identify dual-frequency radars utilizing a W-band channel as promising tools for observing rainfall in the high-latitude climate.Peer reviewe

Machine learning-based fusion studies of rainfall estimation from spaceborne and ground-based radars

2019 Spring.Includes bibliographical references.Precipitation measurement by satellite radar plays a significant role in researching the water circle and forecasting extreme weather event. Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) has capability of providing a high-resolution vertical profile of precipitation over the tropics regions. Its successor, Global Precipitation Measurement (GPM) Dual-frequency Precipitation Radar (DPR), can provide detailed information on the microphysical properties of precipitation particles, quantify particle size distribution and quantitatively measure light rain and falling snow. This thesis presents a novel Machine Learning system for ground-based and space borne radar rainfall estimation. The system first trains ground radar data for rainfall estimation using rainfall measurements from gauges and subsequently uses the ground radar based rainfall estimates to train spaceborne radar data in order to get space based rainfall product. Therein, data alignment between spaceborne and ground radar is conducted using the methodology proposed by Bolen and Chandrasekar (2013), which can minimize the effects of potential geometric distortion of spaceborne radar observations. For demonstration purposes, rainfall measurements from three rain gauge networks near Melbourne, Florida, are used for training and validation purposes. These three gauge networks, which are located in Kennedy Space Center (KSC), South Florida Water Management District (SFL), and St. Johns Water Management District (STJ), include 33, 46, and 99 rain gauge stations, respectively. Collocated ground radar observations from the National Weather Service (NWS) Weather Surveillance Radar – 1988 Doppler (WSR-88D) in Melbourne (i.e., KMLB radar) are trained with the gauge measurements. The trained model is then used to derive KMLB radar based rainfall product, which is used to train both TRMM PR and GPM DPR data collected from coincident overpasses events. The machine learning based rainfall product is compared against the standard satellite products, which shows great potential of the machine learning concept in satellite radar rainfall estimation. Also, the local rain maps generated by machine learning system at KMLB area are demonstrate the application potential

An Integrated Approach to Weather Radar Calibration and Monitoring Using Ground Clutter and Satellite Comparisons

The stability and accuracy of weather radar reflectivity calibration are imperative for quantitative applications, such as rainfall estimation, severe weather monitoring and nowcasting, and assimilation in numerical weather prediction models. Various radar calibration and monitoring techniques have been developed, but only recently have integrated approaches been proposed, that is, using different calibration techniques in combination. In this paper the following three techniques are used: 1) ground clutter monitoring, 2) comparisons with spaceborne radars, and 3) the self-consistency of polarimetric variables. These techniques are applied to a C-band polarimetric radar (CPOL) located in the Australian tropics since 1998. The ground clutter monitoring technique is applied to each radar volumetric scan and provides a means to reliably detect changes in calibration, relative to a baseline. It is remarkably stable to within a standard deviation of 0.1 dB (decibels). To obtain an absolute calibration value, CPOL observations are compared to spaceborne radars on board TRMM (Tropical Rainfall Measuring Mission) and GPM (Global Precipitation Measurement) using a volume-matching technique. Using an iterative procedure and stable calibration periods identified by the ground echoes technique, we improve the accuracy of this technique to about 1 dB. Finally, we review the self-consistency technique and constrain its assumptions using results from the hybrid TRMM-GPM and ground echo technique. Small changes in the self-consistency parameterization can lead to 5 dB of variation in the reflectivity calibration. We find that the drop-shape model of Brandes et al. with a standard deviation of the canting angle of 12 degrees best matches our dataset

Seasonal and Diurnal Variations of Vertical Profile of Precipitation over Indonesian Maritime Continent

This chapter presents variabilities in the vertical structure of precipitation over the Indonesian maritime continent (IMC), which were inferred from the gradients of the radar reflectivity (dBZ) below the freezing level and were gathered from the latest 2A25 TRMM–Precipitation Radar product over a 17-year time span (1998–2014). In general, the downward increasing (DI) pattern of dBZ toward the surface is more dominant than the downward decreasing (DD) pattern, which has a ratio of 1.3. The DI is frequently observed over the ocean, and the higher prevailing rain top heights over land are associated with DD, in most cases. Shallow convective rains have the largest ratio of DI to DD (>4), followed by deep convective rains. The largest ratio is observed during December–January–February (DJF) when wetter conditions are dominant over the IMC, which is a favorable condition for raindrop growth. The stratiform rains show a dominant DD in which the ratio of DD to DI is greater than 1.6 for each season. The spatial distribution of the stratiform gradient is more complex than that of convective rain and does not show a robust land-ocean contrast. The diurnal variation in the reflectivity gradient for stratiform rain is less pronounced. With convective rain, DD is more dominant in the afternoon and evening over a large island, indicates a decrease in the raindrop concentration due to the evaporation and updraft associated with the intense convection

Use of Dual Polarization Radar in Validation of Satellite Precipitation Measurements: Rationale and Opportunities

Dual-polarization weather radars have evolved significantly in the last three decades culminating in the operational deployment by the National Weather Service. In addition to operational applications in the weather service, dual-polarization radars have shown significant potential in contributing to the research fields of ground based remote sensing of rainfall microphysics, study of precipitation evolution and hydrometeor classification. Furthermore the dual-polarization radars have also raised the awareness of radar system aspects such as calibration. Microphysical characterization of precipitation and quantitative precipitation estimation are important applications that are critical in the validation of satellite borne precipitation measurements and also serves as a valuable tool in algorithm development. This paper presents the important role played by dual-polarization radar in validating space borne precipitation measurements. Starting from a historical evolution, the various configurations of dual-polarization radar are presented. Examples of raindrop size distribution retrievals and hydrometeor type classification are discussed. The quantitative precipitation estimation is a product of direct relevance to space borne observations. During the TRMM program substantial advancement was made with ground based polarization radars specially collecting unique observations in the tropics which are noted. The scientific accomplishments of relevance to space borne measurements of precipitation are summarized. The potential of dual-polarization radars and opportunities in the era of global precipitation measurement mission is also discussed

Validation of TRMM Precipitation Radar Through Comparison of its Multi-Year Measurements to Ground-Based Radar

A procedure to accurately resample spaceborne and ground-based radar data is described, and then applied to the measurements taken from the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) and the ground-based Weather Surveillance Radar-1988 Doppler (WSR-88D or WSR) for the validation of the PR measurements and estimates. Through comparisons with the well-calibrated, non-attenuated WSR at Melbourne, Florida for the period 1998-2007, the calibration of the Precipitation Radar (PR) aboard the TRMM satellite is checked using measurements near the storm top. Analysis of the results indicates that the PR, after taking into account differences in radar reflectivity factors between the PR and WSR, has a small positive bias of 0.8 dB relative to the WSR, implying a soundness of the PR calibration in view of the uncertainties involved in the comparisons. Comparisons between the PR and WSR reflectivities are also made near the surface for evaluation of the attenuation-correction procedures used in the PR algorithms. It is found that the PR attenuation is accurately corrected in stratiform rain but is underestimated in convective rain, particularly in heavy rain. Tests of the PR estimates of rainfall rate are conducted through comparisons in the overlap area between the TRMM overpass and WSR scan. Analyses of the data are made both on a conditional basis, in which the instantaneous rain rates are compared only at those pixels where both the PR and WSR detect rain, and an unconditional basis, in which the area-averaged rain rates are estimated independently for the PR and WSR. Results of the conditional rain comparisons show that the PR-derived rain is about 9% greater and 19% less than the WSR estimates for stratiform and convective storms, respectively. Overall, the PR tends to underestimate the conditional mean rain rate by 8% for all rain categories, a finding that conforms to the results of the area-averaged rain (unconditional) comparisons

Effects of Tunable Data Compression on Geophysical Products Retrieved from Surface Radar Observations with Applications to Spaceborne Meteorological Radars

This paper presents results and analyses of applying an international space data compression standard to weather radar measurements that can easily span 8 orders of magnitude and typically require a large storage capacity as well as significant bandwidth for transmission. By varying the degree of the data compression, we analyzed the non-linear response of models that relate measured radar reflectivity and/or Doppler spectra to the moments and properties of the particle size distribution characterizing clouds and precipitation. Preliminary results for the meteorologically important phenomena of clouds and light rain indicate that for a 0.5 dB calibration uncertainty, typical for the ground-based pulsed-Doppler 94 GHz (or 3.2 mm, W-band) weather radar used as a proxy for spaceborne radar in this study, a lossless compression ratio of only 1.2 is achievable. However, further analyses of the non-linear response of various models of rainfall rate, liquid water content and median volume diameter show that a lossy data compression ratio exceeding 15 is realizable. The exploratory analyses presented are relevant to future satellite missions, where the transmission bandwidth is premium and storage requirements of vast volumes of data, potentially problematic

Method to combine spaceborne radar and radiometric observations of precipitation, A

2010 Fall.Includes bibliographical references.This dissertation describes the development and application of a combined radar-radiometer rainfall retrieval algorithm for the Tropical Rainfall Measuring Mission (TRMM) satellite. A retrieval framework based upon optimal estimation theory is proposed wherein three parameters describing the raindrop size distribution (DSD), ice particle size distribution (PSD), and cloud water path (cLWP) are retrieved for each radar profile. The retrieved rainfall rate is found to be strongly sensitive to the a priori constraints in DSD and cLWP; thus, these parameters are tuned to match polarimetric radar estimates of rainfall near Kwajalein, Republic of Marshall Islands. An independent validation against gauge-tuned radar rainfall estimates at Melbourne, FL shows agreement within 2% which exceeds previous algorithms' ability to match rainfall at these two sites. The algorithm is then applied to two years of TRMM data over oceans to determine the sources of DSD variability. Three correlated sets of variables representing storm dynamics, background environment, and cloud microphysics are found to account for approximately 50% of the variability in the absolute and reflectivity-normalized median drop size. Structures of radar reflectivity are also identified and related to drop size, with these relationships being confirmed by ground-based polarimetric radar data from the North American Monsoon Experiment (NAME). Regional patterns of DSD and the sources of variability identified herein are also shown to be consistent with previous work documenting regional DSD properties. In particular, mid-latitude regions and tropical regions near land tend to have larger drops for a given reflectivity, whereas the smallest drops are found in the eastern Pacific Intertropical Convergence Zone. Due to properties of the DSD and rain water/cloud water partitioning that change with column water vapor, it is shown that increases in water vapor in a global warming scenario could lead to slight (1%) underestimates of a rainfall trends by radar but larger overestimates (5%) by radiometer algorithms. Further analyses are performed to compare tropical oceanic mean rainfall rates between the combined algorithm and other sources. The combined algorithm is 15% higher than the version 6 of the 2A25 radar-only algorithm and 6.6% higher than the Global Precipitation Climatology Project (GPCP) estimate for the same time-space domain. Despite being higher than these two sources, the combined total is not inconsistent with estimates of the other components of the energy budget given their uncertainties

Estimation of Raindrop Size Distribution Parameters Using Lightning Data over West Sumatra

In situ observations of raindrop size distributions (DSDs) are still limited, especially in the tropics. Therefore, this study develops an alternative method to calculate DSD parameters by utilizing lightning data from the World-Wide Lightning Location Network (WWLLN) observation. DSD data was obtained from Parsivel's observations in the equatorial regions of Indonesia, i.e., Kototabang (100.32◦E, 0.20◦S, 865 m above mean sea level/ASL), Padang (100.46°E, 0.915°S, 200 m ASL), and Sicincin (100.30°E, 0.546°S, 134 m ASL). A gamma distribution parameterized the DSD. Three analysis domains were examined, with a grid of 0.1° x 0.1°, 0.5° x 0.5°, and 1° x 1°.  We examined the possibility to calculate the near-instantaneous DSD parameter, so three short time intervals, namely, one, five and ten minutes, were used. The results showed that the number of lightning strokes does not adequately correlate with DSD parameters. This is observed in all time intervals and analysis domains. Thus, the use of lightning data to calculate DSD parameters is not possible for short time interval of DSD (near instantaneous DSD). However, lightning data can estimate the average DSD parameters for an average time of more than one hour, as recommended by previous studies