The Geostationary Operational Environmental Satellite-R (GOES-R) series provides new opportunities for continuous observation of precipitation at large scales with a high resolution. An operational quantitative precipitation estimation (QPE) product has been produced based on multi-channel measurements from the Advanced Baseline Imager (ABI) aboard the GOES-16 (formerly known as GOES-R). This paper presents a comprehensive evaluation of this GOES-16 QPE product against a ground reference QPE product from the National Oceanic and Atmospheric Administration (NOAA) Multi-Radar Multi-Sensor (MRMS) system over the continental United States (CONUS) during the warm seasons of 2018 and 2019. For the first time, the accuracy of GOES-16 QPE product was quantified using the gauge-corrected MRMS (GC-MRMS) QPE product, and a number of evaluation metrics were applied to adequately resolve the associated errors. The results indicated that precipitation occurrence and intensity estimated by the GOES-16 QPE agreed with GC-MRMS fairly well over the eastern United States (e.g., the probability of detection was close to 1.0, and the Pearson’s correlation coefficient was 0.80 during September 2019), while the discrepancies were noticeable over the western United States (e.g., the Pearson’s correlation coefficient was 0.64 for the same month). The performance of GOES-16 QPE was downgraded over the western United States, in part due to the limitations of the GOES-16 rainfall retrieval algorithm over complex terrains, and in part because of the poor radar coverage analyzed by the MRMS system. In addition, it was found that the GOES-16 QPE product significantly overestimated rainfall induced by the mesoscale convective systems in the midwestern United States, which must be addressed in the future development of GOES satellite rainfall retrieval algorithms.

Based on air temperature, relative humidity, air pressure, and wind speed data from 4 buoy stations in the northern South China Sea, the homogeneity of the evaporation duct height (EDH) obtained from sea surface temperature (SST) from buoys and SST from the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 Reanalysis (ERA-5) data are compared using the Naval-Postgraduate-School (NPS) model. The findings are given as follows: (1) the SST of the ERA-5 has highly accurate (the difference between the buoys and ERA-5 is mainly falling in –0.5°C~1.5°C) and shows strong correlations (≥0.95) with the observed data. (2) The probability of the EDH difference at –2m~2m obtained from the ERA-5 is lower than the actual observation, which underestimates the uniformity of the EDH. (3) The SST difference of buoys and ERA-5 at 0°C to 1.0°C has a more significant impact on the assessment of non-uniformity of EDH than the SST difference at –0.5°C~0°C and 1.0°C~1.5°C (take up above 60%). In conclusion, although the uniformity of the EDH may be underestimated based on the ERA-5 data, it still demonstrates the uniformity characteristic of the EDH quite well.

The uniformity of evaporation duct height (EDH) plays a crucial role in the accurate prediction of electromagnetic propagation. This paper examines the non-uniformity of EDH in the South China Sea using data from 5 buoy stations and 14 automatic weather stations. The factors contributing to the non-uniformity of EDH are analyzed using the Spearman rank correlation method, and the impact of sea surface temperature (SST) error on the non-uniformity of EDH is also examined. The findings are as follows: (1) the Xisha Islands (XS) show the most non-uniformity in EDH, while the distributions of EDH in the Hainan Island (HN) and Nansha Islands (NS) are fairly uniform. (2) The primary factor related to the non-uniformity of EDH in the HN region is relative humidity when ASTD≥0, and wind speed when ASTD<0. However, the main factor related to the non-uniformity of EDH in the XS and NS regions is atmospheric temperature when ASTD≥0, and wind speed when ASTD<0. (3) The SST errors mainly affect the percentage distribution of EDH difference when its absolute value is less than 2m. When the absolute value of EDH difference is more than 2m, the impact of SST errors is relatively minor.

The velocity/distance trade-off poses a fundamental challenge in pulsed Doppler weather radar systems and is known as the velocity/distance dilemma. Techniques such as multiple-pulse repetition frequency, staggered pulse repetition time (PRT), and pulse phase coding are commonly used to mitigate this issue. The current study evaluates the adaptability/capability of a specific type of low-capture signal called the non-uniform PRT (NPRT) through analyzing the weather target characteristics of typical velocity distributions. The spectral moments estimation (SME) signal-processing algorithm of the NPRT weather echo is designed to calculate the average power, velocity, and spectrum width of the target. A comprehensive error analysis is conducted to ascertain the efficacy of the NPRT processing algorithm under influencing factors. The results demonstrate that the spectral parameters of weather target echo with a velocity of $[-50,50]$ m/s through random-jitter NPRT signals align with radar functionality requirements (RFRs). Notably, the NPRT waveform resolves the inherent conflicts between the maximum unambiguous distance and velocity and elevates the upper limit of the maximal observation velocity. The evaluation results confirm that nonlinear radar signal processing technology can improve a radar’s detection performance and provide a new method for realizing the multifunctional observation of radar in different applications.

Precipitation is a critical element in global atmospheric circulation and ecosystem. However, it is challenging to monitor the global or even continental scale precipitation features using ground-based rain gauges due to the spatial coverage limitations. The Geostationary Operational Environmental Satellite-R (GOES-R) series provide new opportunities for continuous observation of precipitation at large scales. This paper presents a detailed cross-comparison of quantitative precipitation estimates (QPE) between GOES-R (GOES-16) and the ground radar-based rainfall product derived from the National Ocean and Atmospheric Administration's (NOAA) multi-radar multi-sensor (MRMS) system over the continental United States (CONUS). The precipitation detectability of GOES-R is investigated using MRMS products as references. In addition, uncertainties associated with the GOES-R precipitation estimates are quantified in different regions over the CONUS.

Severe convective weather systems, such as supercells, hailstorms, tornadoes, and downbursts, are characterized by large radial velocities (often exceeding $\pm 30$ m/s), broad spectrum widths (4–10m/s), and high reflectivity (30–70 dBZ). These features place stringent demands on the unambiguous velocity, range coverage, and estimation accuracy of Doppler weather radars. Conventional uniform pulse repetition time (PRT) faces an inherent trade-off between the maximum unambiguous range $R_\rm{max}$ and the maximum unambiguous velocity $V_\rm{max}$. To overcome this fundamental limitation, this paper proposes the use of non-uniform pulse repetition time (NPRT) waveforms for enhancing the radar observations of extreme precipitation systems. By randomly jittering the inter-pulse intervals, NPRT transforms the deterministic velocity aliasing of uniform sampling into a noise- like floor that can be separated by a tailored signal processing chain. An optimized Gaussian sliding window adaptation (GSWA) algorithm is developed to extract the true target spectrum from the non-uniformly sampled aliased power spectrum, followed by spectral moment estimation (SME). The performance is evaluated using radar signals simulated from real observations collected by two operational weather radars: a C-band radar (HZ-CPOL) in Hangzhou, China, during a severe convective precipitation event, and an S-band NEXRAD radar (KMLB) in Florida, USA, during Hurricane Milton (2024). Results show that, in the C-band case, NPRT achieves an 84.5% reduction in velocity root mean square error (RMSE) and a 69.8% reduction in spectrum width RMSE compared with uniform PRT. In the S-band hurricane case, NPRT also provides significant improvements, with velocity RMSE reduced by 32.6% and spectrum width RMSE reduced by 85.6%. Under the idealized simulation conditions considered in this study, NPRT demonstrates the potential to resolve velocity aliasing for flows exceeding the Nyquist velocity without sacrificing the unambiguous range or data coverage.