Cross-evaluation of reflectivity from the space-borne precipitation radar and multi-type ground-based weather radar network in China

Atmospheric Research 196 (2017) 200–210

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Cross-evaluation of reflectivity from the space-borne precipitation radar and multi-type ground-based weather radar network in China

MARK

Lingzhi Zhonga,⁎, Rongfang Yangb, Yixin Wenc, Lin Chend,⁎, Yabin Goue, Ruiyi Lif, Qing Zhouf, Yang Hongg,h a

Chinese Academy of Meteorology and Science, China Public Weather Service Center, Hebei Meteorological Bureau, China c Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, Norman, OK, United States d National Satellite Meteorology Center, China Meteorological Administration, Beijing, China e Hangzhou Meteorological Bureau, Hangzhou, China f Meteorological Observation Center, China Meteorological Administration, Beijing, China g State Key Laboratory of Hydro science and Engineering, Tsinghua University, Beijing, China h School of Civil Engineering and Environmental Sciences, University of Oklahoma, Norman, United States b

A B S T R A C T China operational weather radar network consists of more than 200 ground-based radars (GR(s)). The lack of unified calibrators often result in poor mosaic products as well as its limitation in radar data assimilation in numerical models. In this study, radar reflectivity and precipitation vertical structures observed from spaceborne TRMM (Tropical Rainfall Measurement Mission) PR (precipitation radar) and GRs are volumetrically matched and cross-evaluated. It is found that observation of GRs is basically consistent with that of PR. For their overlapping scanning regions, the GRs are often affected by the beam blockage for complex terrain. The statistics show the better agreement among S band A type (SA) radars, S band B type (SB) radars and PR, as well as poor performance of S band C type (SC) radars. The reflectivity offsets between GRs and PR depend on the reflectivity magnitudes: They are positive for weak precipitation and negative for middle and heavy precipitation, respectively. Although the GRs are quite consistent with PR for large sample, an individual GR has its own fluctuated biases monthly. When the sample number is small, the bias statistics may be determined by a single bad GR in a group. Results from this study shed lights that the space-borne precipitation radars could be used to quantitatively calibrate systematic bias existing in different GRs in order to improve the consistency of groundbased weather radar network across China, and also bears the promise to provide a robust reference even form a space and ground constellation network for the dual-frequency precipitation radars onboard the satellites anticipated in the near future.

1. Introduction Weather radars can help us to understand and monitor severe weather in flood seasons. The weather radar platform mainly includes ground-based radars (hereafter GR(s)), airborne radars, and spaceborne radars, all with advantages and disadvantages. Ground-based radars are easily built, especially on flat terrain. Airborne weather radars can be easily hung from aircraft in order to look through precipitation and acquire the structure and characteristics of cloud and precipitation due to its small volume and light weight. Space-borne weather radars can obtain much more information regarding global cloud and precipitation distributions over a wide range, especially in

⁎

Corresponding authors. E-mail addresses: [email protected] (L. Zhong), [email protected] (L. Chen).

http://dx.doi.org/10.1016/j.atmosres.2017.06.016 Received 6 December 2016; Received in revised form 20 April 2017; Accepted 13 June 2017 Available online 15 June 2017 0169-8095/ © 2017 Elsevier B.V. All rights reserved.

areas where ground-based and airborne weather radars are unable to detect. China started construction of the ‘China New Generation Doppler Weather Radar (CINRAD)’ network in 1998, and about 200 radars, consisting of 10-cm (S-band, including A-type (SA), B-type (SB), and C-type (SC)) and 5-cm (C-band) wavelengths have been utilized in operational observations. The S-band CINRAD radars are much like the WSR-88D (Weather Surveillance Radar, 1988, Doppler) units used in the USA, i.e., with approximately a 1° beam width by 1-km range resolution and a volume scan sampling frequency of about 6 min. Each volume scan consists of nine sweeps, with elevation angles ranging from 0.5° (base scan) to 19°. There has been a vast amount of references to the application of

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Loannidou et al., 2016) has systematically studied the difference between PR and GRs, and has suggested that: (1) Bias between the GRs with strict quality controls and PR is small. (2) Reasons for rainfall estimate differences between PR and GRs result from calibration errors, poor quality of the GR reference, differences in scattering theory between two different radar frequencies, volume-matching mismatches, uncertain attenuation correction methods, and inaccurate reflectivityto-rainfall relationships. (3) It is feasible to merge GR and PR observations, especially in mountain areas, where GR beam blockage is severe. This study mainly focuses on the comparison of PR and GRs in China, which is the first step to calibrate historical GRs by using PR dataset. The next section describes the data and methodology. Qualitative comparisons of reflectivity from PR and GRs are given in Section 3, as well as a quantitative comparison between the two data sources in terms of the radar reflectivity at constant altitudes, and separation profiles between convective and stratiform precipitation regions, respectively. The article ends with summary in Section 4.

weather radars in monitoring and forecasting various types of precipitation (Battan, 1973; Wilson and Brandes, 1979; Seo and Smith, 1992; Baeck and Smith, 1998; Davis, 2001; Klimowski et al., 2004; Wang et al., 2009; Zhong et al., 2016; Wapler et al., 2016). Reliable quantitative precipitation estimation (QPE) can provide essential information in order to understand the water cycle and terrestrial hydrologic processes. Factors that affect the accuracy of radar QPE products mainly result from radar calibration bias and retrieval methods (Xiao et al., 2007; Wang and Wolfe, 2009; Wang et al., 2012; Zhou, 2013). During the past two decades, most researchers have been focused on how to improve the hardware making and the retrieval, instead of focusing on the effects that result from calibration errors (e.g., Koistinen, 1991; Joss and Lee, 1995; Seo et al., 2000; Germann and Joss, 2002; Zhang and Qi, 2010; Wang et al., 2012). Now the linking between adjacent GRs (Xiao et al., 2007; Zhou, 2013) and how to improve the accuracy of radar QPE by comparing and correcting GR observations with space-borne radars has become a hot research topic (e.g., Anagnostou et al., 2001; Liao et al., 2001; Liao and Meneghini, 2009; Wang and Wolfe, 2009; Wen et al., 2011, 2013; Cao and Qi, 2014). Houze et al. (2004) found that calibration uncertainty is possibly the most severe problem in generating accurate rainfall products from radar observations. For example, a calibration offset of 2 dB could contribute to an uncertainty of 30% in monthly rainfall estimation (Wang and Wolfe, 2009). Although strict calibration systems are established in order to pursue the stability and accuracy for each GR before its operation, and auto calibration processes are also used to aid single GR to acquire corrected data, long-term operational running will result in systematic offsets (i.e., reflectivity intensity and echo location). Xiao et al. (2007) and Zhou (2013) found that the minimum and maximum differences between adjacent GRs could be 2.3 dBZ and 4.6 dBZ. Thus, the lack of unified calibrations among numerous GRs often result in a discontinuity of adjacent echoes, as well as poor mosaic products, which will limit the utility of radar data assimilation in numerical models. The precipitation radar (PR) onboard the Tropical Rain fall Measuring Mission (TRMM) has a 13.8-GHz frequency (2.2-cm wavelength), with a field-of-view (FOV) diameter of about 5.0 km (after the boost in August 2001) at the nadir and a 0.25 km range resolution. The radar has a nominal sensitivity of approximately 18 dBZ (Simpson et al., 1996; Wen et al., 2013). Both internal and external calibrations have shown that the PR is consistently able to measure reflectivity with an absolute calibration accuracy better than ± 1 dB (Kawanishi et al., 2000; Kummerow et al., 2000; Kozu et al., 2001; Wang, 2001; Takahashi et al., 2003). Thus, PR can be used as an assistant reference source to help calibrate GR observations and to compliment observation gaps between adjacent GRs. On the other hand, due to the operating frequency, PR echoes can be greatly attenuated by heavy precipitation. Thus, GRs with correct calibrations can be used to check PR attenuation correction algorithm performance and rain detection ability near the ground. Some previous work has investigated comparison and correction GRs using PR observations, as well as estimation of PR products and algorithms by GRs. Schumacher and Houze (2000) compared the echo structure and found that PR can capture the main features of rain, but will miss weaker echoes. Bolen and Chandrasekar (2003) used a geometric matching method to analyze and correct US ground-based radar observation bias. Wang and Wolfe (2009) noted that the PR attenuation correction algorithm for convective precipitation is good, but there is a slight over-correction for stratiform precipitation. Amitai et al. (2009) performed a comparison of probability density functions (PDFs) of instantaneous rain rates between the PR and GR, and showed that the PDFs of PR are generally shifted toward lower rain rates. Liao and Meneghini (2009) used observations from Melbourne and Florida, and found that PR attenuation is underestimated in convective rain events, but is accurately corrected in stratiform rain. In recent years, some research (Anagnostou et al., 2001; Liao et al., 2001; Wang and Wolfe, 2009; Wen et al., 2011; Cao et al., 2013; Wang et al., 2015;

2. Data re-sampling and matching 2.1. Data In this paper, we compare the radar reflectivity of the GRs shown in Fig. 1, with PR from the flood season (June, July, and August (JJA)) of 2011 and 2012. For the past few years, a great deal of effort from scientists has been put into generating good quality products with TRMM PR (e.g., Iguchi et al., 2000, 2009; Schumacher and Houze, 2000; Takahashi et al., 2003; Seto and Iguchi, 2007; Awaka et al., 2009). The latest version of the TRMM algorithm, version 7 (TRMM V7), was released in July 2011, by the TRMM science team. Compared to former versions, TRMM V7 has been greatly improved (Seto and Iguchi, 2007; Awaka et al., 2009; Iguchi et al., 2009; Cao et al., 2013). For example, the V7 algorithm has increased the subcategories of rain types and refined the classification products (Awaka et al., 2009). The 2A25 algorithm produces enhanced radar reflectivity profiles by improving path integrated attenuation estimations and refining the attenuation correction method (Meneghini et al. 2004; Iguchi et al., 2009). Rain estimation is now improved with the application of a new drop size distribution (DSD) model. Non-uniform beam filling (NUBF) correction was also reintroduced in V7. The attenuation corrected reflectivity of 80 layers with the entire height at 0–20 km above mean sea level (MSL), and the rain type classification products from TRMM V7 datasets (Cao et al., 2013), are used in this research. 2.2. Matching GRs and PR Temporal and spatial matching between the 3D mosaic reflectivity data and the attenuation-corrected PR reflectivity profiles are performed in order to make comparisons. The matching scheme is similar to those that have been previously published in the literature (e.g., Heymsfield et al., 2000; Schumacher and Houze, 2000; Anagnostou et al., 2001; Liao et al., 2001; Houze et al., 2004; Wang and Wolfe, 2009), regardless of the technical differences among these studies. In this paper, matching GR and PR consists of three steps: The Sband radars complete one entire volume scan every six minutes and obtain 10 base reflectivity data points per hour. Firstly, following Zhang et al. (2005) and Xiao and Liu (2006), we qualify and convert the original 6-min base reflectivity, with the polar coordinates, into three-dimensional Cartesian coordinate data, using the nearest neighbor method at the radial and azimuth orientations (Jorgensen et al., 1983), and the linear interpolation method on the vertical levels (Mohr and Vaughn, 1979; Miller et al., 1986; Xiao and Liu, 2006) with a horizontal resolution of 1 km × 1 km and a vertical resolution of 0.5 km. Each PR scan only lasts about 0.6 s, whereas the GR volume scan lasts about 6 min. We identify the TRMM PR overpasses that correspond to 201

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Fig. 1. Distribution of three types of weather radars mentioned in this research as well as the terrain height.

methodology was to evaluate the systematic differences between PR and GRs. Several statistical indices were selected for evaluating GRs observation using PR as the reference. The Pearson correlation coefficient (CC) is used to assess the agreement between PR and GRs observations. The mean bias (Bias) expresses the mean offset status between GRs and PR. The relative bias (RE) (i.e., the bias in percent) is used to assess the systematic bias of PR observations. The mean absolute error (MAE) measures the average magnitude of the error. The root mean-square error (RMSE) also measures the average error magnitude, but gives a greater weight to the larger errors.

coincident overpasses by TRMM PR and meet the following criteria: 1) the maximum time discrepancy between GR and PR observations is less than 10 min, 2) PR and GRs meteorological echo overlapping area are within 200 km of GRs' scanning, 3) the maximum space discrepancy between GR and PR is less than ± 0.1° in longitude and latitude, 4) all reflectivity chosen for study should be ≥ 18 dBZ (Wang et al., 2015; Wen et al., 2011). Secondly, the simultaneous GR 3D reflectivity and TRMM PR data were re-sampled using the average matching method (AMM) on the same grid, using the lower horizontal resolution (10 km × 10 km) of the two datasets. To avoid the averaging biases associated with dBZ, we performed averaging on the linear reflectivity (Z) instead of dBZ. Once the averaging was completed, the linear units were converted back to logarithmic ones. The last step was to match the re-sampled GR and PR by considering their different vertical resolutions. The vertical resolutions of re-sampled GR and PR were 500 m and 250 m, respectively. For the single ground radar, the reflectivities of GR and PR are marked as R_GR(i) and R_PR(i), where i stands for their vertical level. We choose the minimum difference related to the R_GR(i) from R_PR(i-1), R_PR(i), and R_PR(i + 1) as a matched pair. The matched, gridded reflectivity from the PR observations and the GR datasets were then compared, both qualitatively and quantitatively, in the following sections.

N

N {∑i = 1

Bias =

1 N

[dBZGR (i) −

N dBZGR]2 ∑i = 1

− −

[dBZPR (i) − dBZPR]2 }

∑ (dBZGR − dBZPR)

1

2

(1)

(2)

N

N

RE =

− −

∑i = 1 [dBZGR (i) − dBZGR][dBZPR (i) − dBZPR]

CC =

N

∑i = 1 [dBZGR (i) − ∑i = 1 dBZPR (i)] N

∑i = 1 dBZPR (i)

× 100% (3)

N

MEA =

2.3. Methodology

∑i = 1 |dBZGR (i) − dBZPR (i)| (4)

N N

1

2 ⎡ ∑ |dBZGR (i) − dBZPR (i)| ⎤ RMSE = ⎢ i = 1 ⎥ N ⎣ ⎦

The above-mentioned matching scheme can minimize the uncertainties associated with the sampling resolution differences of GRs and PR. The PR has been demonstrated by the National Space and Development Agency of Japan (NASDA) to be consistent with calibration stability within 1.0 dB (Wang et al., 2015; Kawanishi et al., 2000; Takahashi et al., 2003). PR observations can be taken as the reference for the evaluation of GRs. Based on this, the final step of the

2

(5)

where dBZGR and dBZPR stand for the reflectivity of GR and PR, respectively. N is the total number of the matched pairs. When the sample size is sufficiently large, the mean offset follows a Gaussian distribution according to the central limit theorem. However, only providing the mean offset value is not sufficient unless it is accompanied by its linear 202

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(a)

(b)

(c)

(d)

(e)

(f)

Fig. 2. Echo reflectivity (unit: dBZ) compared from PR (right) and GRs (left), (a) and (b) are for SA, (c) and (d) are for SB, and (e) and (f) are for SC, the dashed line show the PR tracks.

appearance of the PR field is obvious, both the location and reflectivity intensity magnitudes of the precipitation echoes from the two radars are quite consistent, except the intensity is a little weaker in NJ than it is in PR. Echoes less than 18 dBZ in the NJ radar are not shown in PR because the sensitivity of the latter is greater than 18 dBZ. Fig. 2c, d shows an example of horizontal reflectivity distributions (at a 3-km level) of the PR and SB-GuiLin (GL) radar for a precipitation case (21 August, 2012). It is obvious that there is large and significant precipitation echo in PR for the mid-western area of the overlapping coverage. Most of the area (especially the region far away from radar site) has no precipitation echo, except for the south region for the GL radar. The reason for this is that the lower-level GL radar beam is blocked by the complex terrain. The overlapped precipitation echoes

regression 95% confidence interval.

3. Results 3.1. Qualitative comparison between pr and ground radars In this section, the matched cases of PR and ground-radar reflectivity data are presented by showing the degree of agreement between the two radar reflectivity spatial patterns and relative magnitudes. Fig. 2a, b shows the instantaneous snapshots of the horizontal reflectivity distributions (at a 2-km level) from PR and SA-NanJing (NJ) radar for a case (3 June 2011) with significant precipitation coverage in the overlapping area of the two radars. Though the smoother 203

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(a)

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50

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CC=0.80485 Bias=0.52541 RE=1.983% MAE=2.5481 RMSE=3.5782 Points number=929088 Y=0.76579*X+6.7312

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GR-SB

All

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All CC=0.52396 Bias=-1.1394 RE=-4.1013% MAE=4.0931 RMSE=5.6204 Points number=406085 Y=0.49625*X+12.8556

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80 60

40 40 30

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100 1:1 Line Linear Regression 40 50 60 70 PR

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CC=0.78147 Bias=-0.87387 RE=-2.5357% MAE=3.5296 RMSE=4.776 Points number=259757 Y=0.75449*X+7.5872

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CC=0.78235 Bias=0.69229 RE=2.648% MAE=2.4841 RMSE=3.4076 Points number=567008 Y=0.75465*X+7.1067

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CC=0.68086 Bias=0.88653 RE=3.4196% MAE=2.8729 RMSE=4.0301 Points number=2168745 Y=0.68169*X+9.1388

GR-SB

Stratiform

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Stratiform CC=0.40565 Bias=-1.2084 RE=-4.523% MAE=3.7343 RMSE=5.0998 Points number=217421 Y=0.42237*X+14.2243

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0

Fig. 3. Scatter plot density compared by PR and GRs for all precipitation (a), convective precipitation (b), and stratiform precipitation (c) of SA-type (left), SB-type (middle), and SC-type (right) radar.

blockage caused by the complex terrain, especially for the lower levels far from the GR site. (2) Though the re-sampling and matching methods are used, PR and GR could not simultaneously observe the precipitation system completely. Overall echo figures observed from both GRs and PR are quite consistent, and the reflectivity intensity is stronger in PR than it is in GRs. For regions with complex terrain, PR radar, which is not affected by beam blockage, could be used for merging and improving GRs observations.

from both radars are quite consistent. Fig. 2e, f shows an example of horizontal reflectivity distributions (at a 3-km level) of the PR and SCNanChong (NC) radar for a precipitation case (17 July, 2012). The echo intensity is stronger in PR than it is in the NC radar; in addition, the effect of the radar beam blockage in the mid and northern region is far away from the latter. For the scan coverage of space-borne radar and ground radar are often not the same completely, there is some difference on the instantaneous snapshots of reflectivity echoes between PR and GRs. For example, in the area of 32.1°N–32.5°N and 105.5°E–107°E, PR captured the precipitation information (Fig. 2f), while GR didn't for it is out of the coverage (Fig. 2e). In the area of 30.5°N–31.2°N and 104.5°E–105.5°E, GR got the precipitation echo, but PR didn't. The shapes of the echoes in the two radars are consistent, but the location in the NC radar which shows a minor shift from north to south compared to PR may be resulted from wind. The reasons that result in the differences between the GR and PR figures may be: (1) radar beam

3.2. Statistical analyses This section presents quantitative comparisons between PR and ground radar observations for the data periods and sites in Fig. 1. Fig. 3 gives the scatter plot density of different rain types for the SA, SB, and SC radars. PR classification products are used as the criterion here. TRMM V7 of 2A25 products apply to more than 30 subcategories for the 204

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Fig. 4. Probability density function (PDF) compared from PR and GRs with all height levels (left) and below 4 km levels (right), (a) and (b) are for SA, (c) and (d) are for SB, and (e) and (f) are for SC, dashed and doted lines stand for PR and GR respectively.

weak precipitation and weaker for middle and heavy precipitation, and the mean offset of GRs is nonlinear. Fig. 4 gives the probability density function (PDF) of GRs and PR for all levels (0.5–10 km) and below (including) 4 km. We can see that the distribution of SA is quite consistent with PR, and that the greatest discrepancy occurs with a reflectivity of less than 25 dBZ. Obviously, there are many more number distributions between 20 and 26 dBZ (in PR), and fewer number distributions between 27–35 dBZ. The distribution curve of SB is similar to SA's curve, with a 1–2 dBZ translation of the PR peak, which makes much more numbers distribute within 19–24 dBZ and less numbers distribute within 25–33 dBZ, respectively. Below 4 km, the curves of both SA and SB are quite consistent with those of PR. Differently, the curve of SC radars distributes with about a 3-dBZ translation shift, in any level, according to that of PR, which shows that a systematic bias between observations from SC radars and PR exists. The results demonstrate that: (1) The differences between GRs and PR are mainly caused by the low sensitivity of PR, as well as the calibration accuracy and systematic bias of GRs. (2) Scatter affection during the melting layer. There are a number of large ice particles in the freezing layer, which will affect both GRs and PR. (3) Observations from both GRs and PR are quite consistent at lower levels. As shown in Table 1, the radar reflectivities of the SA and SB radars

classification of rain types. These subcategories can be summarized with several major types: “Stratiform”, “stratiform maybe”, “convective”, “convective maybe”, and “others”. The stratiform type is identified when the bright band (BB) is clearly detected and the vertical profile of reflectivity (VPR) reveals apparent features of stratiform precipitation. When the BB does not exist, and the VPR reveals convective characteristics, the precipitation will be classified using the convective type. When the BB is not clearly detected, the precipitation will be assigned as either convective maybe or stratiform maybe, with the latter distinction being tied to precipitation structure (Steiner et al., 1995; Awaka et al., 2009; Cao et al., 2013). Hereafter, we only consider “convective” and “stratiform” for data analyses. For SA and SB radars, both their observations are quite agreement with PR. The scatters are distributed nearly symmetrically along the diagonal lines. Different from these, the correlation coefficients of SC radars decrease to 0.4–0.5, and the bias is about two times that of SA and SB radars, especially for the convective precipitation (− 4.58 dBZ for SC radars, and −1.2 dBZ for the SA and SB radars). The RMSE for SC is about 1.8 dBZ larger than that of SA and SB, which reveals that GR-PR matching points with large offsets exist. From bias and RMSE we can find that Fig. 3 provides the mean performance of each type of GRs. The scatters show that the reflectivity of GRs is stronger than PR for 205

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Table 1 Statistics parameters of GR and PR matching points. Radar type

SA

SB

SC

Height (km)

1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8

Statistic parameters

Points

CC

RE (%)

Bias (dBZ)

MAE (dBZ)

RMSE (dBZ)

0.73 0.79 0.80 0.81 0.81 0.78 0.74 0.73 – 0.82 0.82 0.81 0.81 0.80 0.80 0.80 – 0.54 0.55 0.52 0.51 0.53 0.53 0.52

−3.60 −2.75 −2.05 −1.63 −1.94 0.37 2.96 3.93 – −4.89 −2.33 0.89 −1.40 −0.06 2.09 2.97 – −4.57 −5.24 −5.48 −5.70 −5.06 −3.91 −3.48

− 1.06 − 0.80 − 0.59 − 0.46 − 0.55 0.10 0.79 1.02 – − 1.47 − 0.68 − 0.26 − 0.41 − 0.02 0.56 0.77 – − 1.36 − 1.52 − 1.58 − 1.64 − 1.43 − 1.08 − 0.95

3.29 2.82 2.65 2.57 2.52 2.64 2.91 2.98 – 2.58 2.44 2.51 2.48 2.47 2.58 2.59 – 4.51 4.35 4.45 4.36 4.13 4.00 4.00

4.26 3.82 3.64 3.53 3.5 3.72 4.09 4.19 – 3.62 3.43 3.49 3.49 3.49 3.61 3.62 – 6.11 5.70 5.78 5.72 5.54 5.52 5.60

1023 18,900 69,415 157,845 283,952 425,086 543,980 621,235 < 400 1896 11,445 32,734 66,655 108,559 147,385 174,572 < 400 1160 8058 22,203 40,459 56,346 65,521 69,442

observation extracted from Fig. 6 is that the reflectivity difference histograms have shapes of a skewed distribution, with a range between − 15 dBZ and + 18 dBZ which means there are systematic biases between GRs and PR. This variability is attributed to the random effects associated with the hydrometeor size distribution variability, residual attenuation correction errors, incomplete knowledge of the ground radar beam propagation, and re-sampling and matching errors. Nevertheless, one can clearly distinguish the GR sites, which have (or not have) systematic differences relative to the PR observations. For example, both the offsets of the WH radar in August 2011 and the CS radar in June 2012 were nearly zero, while the offset of the HF radar in June 2012 was–1.5 dBZ. Most of radars in Fig. 6 have weaker reflectivity values than PR, except for the JH radar (+2.0 dBZ). For the MY radar, the offsets in July 2011 and June 2012 are − 2.3 dBZ and − 0.2 dBZ, respectively. Another issue associated with ground radar bias is its temporal variability, which is due to gradual degradations of system performance (gain, loss, antenna, etc.) or changes in system characteristics (e.g., changing electronics, human factor).We demonstrate time variations of the mean bias (in dBZ) for the WH, HF, JH, and MY radars in the JJA season (June, July, August) between 2011 and 2012 (Fig. 7). There is no offset plotted in June 2011 (Fig. 7d) because of missing data from the MY radar. The mean offset and its 95% confidence interval (the vertical bar), as well as the median and mode offsets, are plotted in these figures. A large confidence interval means a great deal of uncertainty in the estimated mean offset. In addition to the mean, the mode and median can also be chosen to measure GR calibration offsets. Unlike the mean, the mode and median are less sensitive to a small number of outliers. However, the mode offset in Fig. 7 is sometimes quite different from the mean and median offsets because the mode can be incorrectly presented in a rough frequency distribution with multiple peaks. This occasionally happens, especially when the sample size is small. One observation extracted from Fig. 7 is that the offset shows noticeable spatial variations from site to site, and temporal variation month to month. We can also find the difference between adjacent ground radars. For example, the offsets of the WH radar and the HF radar in June 2011 are − 1.0 dBZ and −1.5 dBZ respectively. Thus, we find that the average difference between the two radars is 0.5 dBZ, which is much less than the result reported by Xiao et al.

are highly correlated with PR. The average of CC for SA between 2 km and 6 km (MSL) is about 0.80. RE increases from 2 km to 5 km, and then decreases above 5 km. The statistics have the worst values (CC 0.73, RE − 3.6%, MAE 3.29 dBZ, and RMSE 4.26 dBZ) at 1 km relative to the other layers, which may result from the ground clutter, the attenuation algorithm near the surface of PR, as well as the small matched samples. Thus, results within 2 km could not be used to judge the consistent performance of GRs and PR. The CC of SB is larger than that of SA in every layer, while the RE, MAE, and RMSE are much lower. This reveals that SB radars are more consistent with PR than SA radars. Different from SA and SB, the CC of SC in every layer is much lower (the largest value being only 0.54), and the other statistics are much larger. The mean bias of SC compared to PR is about −3.2 dBZ. Most of the negative bias for GRs demonstrates that the observation of ground radars is often weaker than PR. This conclusion is similar to the results from Wang and Wolfe (2009) in the US, and they found that the PR attenuation algorithm was often over corrected in stratiform precipitation. Fig. 5 shows a comparison of the scatter plot densities between the offsets (offset = dBZGR − dBZPR) and PR for individual GRs. It shows that most of the reflectivities from the WuHan (WH), HeFei (HF), MianYang (MY), and ChangSha (CS) radars are smaller than that from PR, while the reflectivities from JinHua (JH) are stronger. The negative offset in Fig. 5 is nearly bound to a line offset = dBZPR − 18. The positive offset is not obviously bound, but seems to be parallel to the linear line. Although the mean offset changes are in a very narrow range from − 3 dBZ to +3 dBZ, the individual offset can be in a very wide range from –20 dBZ to + 20 dBZ at different reflectivity magnitudes. Higher reflectivities are prone to suffer larger offsets and vice versa. The positive and negative offsets sum to small mean offsets. The offset is dependent on the reflectivity magnitude, and we express their correlation by the solid regression lines in Fig. 5. This is similar to the results from Wang and Wolfe (2009) and Wang et al. (2015), but different from those of Anagnostou et al. (2001). It is noted that scatters in the MY radar are larger than in the other GRs, which may be caused by poor calibration, as well as a small statistic sample. Fig. 6 presents the ground radar PR reflectivity difference histograms for randomly selected individual GR sites in Fig. 1. The first 206

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3.3. Discussion

(2007), who found that the difference between WH and HF was − 2.6 dBZ in July 2004. We can demonstrate that the consistency of the adjacent WH and HF radars is much better now than at that time. In Fig. 7d, the offsets of the MY radar fluctuate within 1 dBZ, most of the time, except for in July 2012 (about − 1.7 dBZ). The WH radar has stable performance. The mean offsets of the HF radar and the JH radar are negative and positive (1.6 dBZ), respectively. This clearly indicates the systematic differences of GR observations against PR measurements. Hence, PR can be utilized as a consistent reference to provide the performance of various GRs.

The differences between PR and GRs can be attributed to following factors: (1) The error of re-sampling and the matching method. PR horizontal and vertical resolutions are 4–5 km and 250 m, respectively. While the GR horizontal and vertical resolutions in the lower levels are 1 km and 500 m, the actual resolution could be lower where it is far from the GR site because of the beam width broadening effect. For example, the beam width of the GR radar is about 1°, and the vertical resolution is about 2 km at a distance of 100 km or further from the 207

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PR reflectivity. This is why the statistics are poor in convective precipitation (Fig. 3). Even though the NUBF correction algorithm is greatly improved in TRMM V7, its validity has not yet been verified. Although the GR has a good horizontal resolution, its vertical resolution decreases with increasing range. The affection of NUBF to GRs could not be ignored. (5) Calibration offset and poor quality control algorithms. The offsets usually fluctuate with time. The ground clutter and noises will also pollute the datasets. (6) Sample size for statistics. When the sample number is small, the average performance can be affected by individual poor GRs in a group. In this study a degrade resolution method was made to match the observations scales. It basically consists in averaging ground radar observations to TRMM's one. Though it might enable to unveil some relevant features to look for the variability observed by ground radars within a “TRMM pixel”, this method has its own advantages: 1) It can mostly reduce the impact of errors resulted from gridding datasets, 2) Solves the problems when the precipitation system moves quickly, 3) Make sure that all precipitation samples are homogeneous.

radar site. (2) The impact of non-Rayleigh scattering is discussed in Wang and Wolfe (2009). In general, large echo intensity values correspond to a high density of large precipitation particles. Due to the PR radar wavelength being only 2.17 cm with respect to the longer wavelength S-band (10 cm) radars, the radar backscatter observations for heavy rain particles of these two bands are quite different. Although the non-Rayleigh effect is not significant when the reflectivity is less than 35 dBZ (Anagnostou et al., 2001), Bolen and Chandrasekar (2000) used raindrop model simulations of these two bands of non-Rayleigh scattering to determine when the echo intensity increases to 40–50 dBZ, and the PR intensity can be greater than 2 dB relative to the GR intensity. (3) The affection of attenuation. The PR operates at a frequency of 13.8 GHz and suffers from significant attenuation at lower levels. Although products of attenuation corrected to reflectivity in PR-2A25, which are considered to be of high quality, and are used in this study, they are still not perfect, especially for levels within 1.2 km due to the ground clutter (Wang and Wolfe, 2009; Liao et al., 2001; Cao et al., 2013). (4) The impact of non-uniform beam filling ((NUBF) (Kozu and Iguchi, 1999; Seto and Iguchi, 2007; Awaka et al., 2009; Iguchi et al., 2000, 2009; Wang and Wolfe, 2009; Cao et al., 2013; Wang et al., 2015). PR horizontal resolution is 4–5 km, and convective cell scales are often smaller than this. Therefore, NUBF has a systematic effect on

4. Conclusions This study provides both qualitative and quantitative cross208

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limited. Currently, the Global Precipitation Measurement (GPM) is called the post-TRMM satellite and carrying Ku/Ka band dual-frequency radars with the advantage of wide coverage (68°N–68°S) and observing the detail structures of cloud and precipitation as well as hydrometer particle phases (Schwaller and Morris, 2011; Tang et al., 2015). The CINRAD operational networks can make full use of spaceborn precipitation radars in establishing a Ground-Space Weather Radar Cross Calibration System (GSWRCCS) by combing TRMM/PR and GPM/DPR together. First, long-term of PR datasets could be used to analyze the reasons result in offsets of every single GR station, and recalibrated historical 3D GR reflectivity datasets need to be set up. Second, GPM system could be used as a real-time cross calibration benchmark. Finally, the GPM space-borne radars and the recalibrated ground-based radars can form a reference for the anticipated precipitation radars carried on future satellites. All of these space and ground radar networks, if coordinately utilized, will provide high quality datasets for broad weather monitoring and forecasting.

evaluation of TRMM PR reflectivity with respect to ground-based weather radars in China by using AMM matching method. Both datasets are re-sampled into a three-dimensional Cartesian coordinate centered at each GR with 10 km × 10 km horizontal and 0.5 km vertical resolution, strictly limited to reflectivity larger than 18 dBZ for comparisons with scatters of different rain types. The main findings are summarized as: The location and reflectivity magnitudes of precipitation echoes in GRs and PR are quite consistent, and the reflectivity intensity of the former is weaker most of the time. A few GRs get stronger reflectivity than PR (for example, JH radar). For their overlapping region with complex terrain, GRs are prone to be effected by the beam blockage. The statistics demonstrate that observation from SB radar is better consistent with PR than the other two. The average offsets of SC radars are − 2.7 dBZ–− 3.2 dBZ. Observations from both GRs and PR are quite consistent during the rain region below the melting layer. The offsets are relative to the reflectivity magnitudes. Each radar has its own bias, and the bias varies with time. When the sample is small, their error statistics may be determined mostly by a single radar with very poor performance in the group. It is a common phenomenon that ground-based weather radar observations are inconsistent. For the future operational radar net, it is possible to solve part of calibration problems by update the precise instruments. A key point is that we must find a stable reference for calibrating the history CINRAD datasets, as well as meet the needs for developing radar climatology research. The current method introduced in this paper focuses on how to evaluate the consistency and continuity of historical ground radar net by using long-term PR datasets. In another word, once the hardware is update and re-calibrated, the radar constant will change as well as the observation biases. The radar maintenance records should also be considered when processing the historical GR datasets. In the near future, a calibration methodology and software system could be developed to evaluate the status and correct the systematic offsets of historical GR observations by using the unified PR, which will provide quality consistent database for establishing accurate long-term retrieval products. The orbit of TRMM (35°N–35°S) as well as its coverage and the ability of detecting solid and mixed phase particles are

Acknowledgements This study was sponsored by the National Natural Science Fund (No. 91437214) and the Third Tibetan Plateau Atmospheric Scientific Experiment (GYHY201406001). All the authors thanks for the support of ground-based weather radar datasets from Chinese Academy of Meteorology and Science and National Information of Meteorological Center. We are also grateful of NASA TRMM mission and appreciate NASA scientists and engineers who have made the TRMM-PR data available. Thanks for the help from Dr. Zhiqiang Zhang, Yanjiao Xiao, Hongping Yang, Prof. Liping Liu, Yuchun Gao, Runsheng Ge and etc. References Amitai, E., Llort, X., Sempere-Torres, D., 2009. Comparison of TRMM radar rainfall estimates with NOAA next-generation QPE. J. Meteorol. Soc. Jpn. 87A, 109–118. Anagnostou, E.N., Morales, C.A., Dinku, T., 2001. The use of TRMM precipitation radar observation in determining ground radar calibration biases. J. Atmos. Ocean. Technol. 18, 616–628. Awaka, J., Iguchi, T., Okamoto, K., Levizzani, V., Bauer, P., Turk, F.J. (Eds.), 2009. TRMM

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