'bright band' で全テキストを検索。[2010-09-16]以降。10/24’11
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Polarimetric Estimates of a 1-Month
Accumulation of Light Rain with a 3-cm Wavelength Radar L. Borowska, D. Zrnić, A. Ryzhkov, P. Zhang, C.
Simmer Journal of Hydrometeorology Volume 12, Issue 5 (October 2011) pp. 1024-1039 doi: 10.1175/2011JHM1339.1 The authors evaluate rainfall estimates from the new
polarimetric X-band radar at Bonn, Germany, for a period between mid-November
and the end of December 2009 by comparison with rain gauges. The emphasis is
on slightly more than 1-month accumulations over areas minimally affected by
beam blockage. The rain regime was characterized by reflectivities
mainly below 45 dBZ, maximum observed rain rates of
47 mm h−1, a mean rain rate of 0.1 mm h−1, and brightband
altitudes between 0.6 and 2.4 km above the ground. Both the reflectivity
factor and the specific differential phase are used to obtain the rain rates.
The accuracy of rain total estimates is evaluated from the statistics of the
differences between radar and rain gauge measurements. Polarimetry
provides improvement in the statistics of reflectivity-based measurements by
reducing the bias and RMS errors from −25% to 7% and from 33% to 17%,
respectively. 【ポイント】Essential to this improvement is separation of the data into those attributed to pure rain, those from the bright band, and those due to nonmeteorological scatterers. A type-specific (rain or wet snow) relation is applied to obtain the rain rate by matching on the average the contribution by wet snow to the radar-measured rainfall below the bright band. The measurement of rain using specific differential phase is the most robust and can be applied to the very low rain rates and still produce credible accumulation estimates characterized with a standard deviation of 11% but a bias of −25%. A composite estimator is also tested and discussed. [Abstract]
[Full
Text] [PDF (1516 KB)] [Add
to Favorites] Xバンドで11月中旬から12月まで1か月以上の降水について地上と偏波レーダを比較した。 偏波を用いると精度が向上した。ポイントは、降水からのエコーとブライトバンドからエコーと非降水エコーを分離したことである。降水判別を適用して、融解層下でレーダが観測する降水量へ、湿った雪の平均的な寄与を合わせこみ、降水量を求めた。【霙が混じっている場合には、平均的＝気候的な換算で降水量に戻した？】φdpを用いた降水量観測はしっかりした技術であり、非常に少ない雨にも応用可能で、積算降水量を計算するのに信頼できる。標準偏差は11％であるが、バイアスは-25％となる。10/27’11 |
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Reducing the Biases in Simulated Radar Reflectivities from a Bulk Microphysics Scheme: Tropical
Convective Systems Stephen E. Lang, Wei-Kuo Tao,
Xiping Zeng, Yaping Li Journal of the Atmospheric Sciences Volume 68, Issue 10 (October 2011) pp. 2306-2320 doi: 10.1175/JAS-D-10-05000.1 A well-known bias common to many bulk microphysics schemes currently being used in cloud-resolving models is the tendency to produce excessively large reflectivity values (e.g., 40 dBZ) in the middle and upper troposphere in simulated convective systems. The Rutledge and Hobbs–based bulk microphysics scheme in the Goddard Cumulus Ensemble model is modified to reduce this bias and improve realistic aspects. Modifications include lowering the efficiencies for snow/graupel riming and snow accreting cloud ice; converting less rimed snow to graupel; allowing snow/graupel sublimation; adding rime splintering, immersion freezing, and contact nucleation; replacing the Fletcher formulation for activated ice nuclei with that of Meyers et al.; allowing for ice supersaturation in the saturation adjustment; accounting for ambient RH in the growth of cloud ice to snow; and adding/accounting for cloud ice fall speeds. In addition, size-mapping schemes for snow/graupel were added as functions of temperature and mixing ratio, lowering particle sizes at colder temperatures but allowing larger particles near the melting level and at higher mixing ratios. The modifications were applied to a weakly organized continental case and an oceanic meso scale convective system (MCS). Strong echoes in the middle and upper troposphere were reduced in both cases. Peak reflectivities agreed well with radar for the weaker land case but, despite improvement, remained too high for the MCS. Reflectivity distributions versus height were much improved versus radar for the less organized land case but not for the MCS despite fewer excessively strong echoes aloft due to a bias toward weaker echoes at storm top. [Abstract]
[Full
Text] [PDF (2488 KB)] [Add
to Favorites] 微物理モデルで対流雲を計算すると雲の上層・中層でZが非常に大きくなるという問題がある。ゴダッードのモデルを改良し現実的な値となるように調整した。調整には雪と霰の雲粒付着、雪の雲氷の収集効率を下げることを含んでいる。雪から霰への雲粒付着を小さくし、昇華も含めている。 変更は大陸性の雲、海洋性の雲（MCS）に適用した。上層の強エコー域は抑えることができた。弱い大陸性の雨についてはZのピークがレーダ観測と一致したが、海洋性のエコーではZは強いままであった。10/24’11 |
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A Review of Quantitative Precipitation
Forecasts and Their Use in Short- to Medium-Range Streamflow
Forecasting Lan Cuo, Thomas C.
Pagano, Q. J. Wang Journal of Hydrometeorology Volume 12, Issue 5 (October 2011) pp. 713-728 doi: 10.1175/2011JHM1347.1 Unknown future precipitation is the dominant source of uncertainty for many streamflow 【河川流量】forecasts. Numerical weather prediction (NWP) models can be used to generate quantitative precipitation forecasts (QPF) to reduce this uncertainty. The usability and usefulness of NWP model outputs depend on the application time and space scales as well as forecast lead time. For streamflow nowcasting (very short lead times; e.g., 12 h), many applications are based on measured in situ or radar-based real-time precipitation and/or the extrapolation of recent precipitation patterns. QPF based on NWP model output may be more useful in extending forecast lead time, particularly in the range of a few days to a week, although low NWP model skill remains a major obstacle. Ensemble outputs from NWP models are used to articulate【a;歯切れの良いvt;明確にする】 QPF uncertainty, improve forecast skill, and extend forecast lead times. Hydrologic prediction driven by these ensembles has been an active research field, although operational adoption has lagged behind. Conversely, relatively little study has been done on the hydrologic component (i.e., model, parameter, and initial condition) of uncertainty in the streamflow prediction system. Four domains of research are identified: selection and evaluation of NWP model–based QPF products, improved QPF products, appropriate hydrologic modeling, and integrated applications. [Abstract]
[Full
Text] [PDF (601 KB)] [Add
to Favorites] 流量の予測で、最も大きな不確実要因は将来の降水量である。数値モデルを用いて、予測降水量を定量的に求めているが不可実性は残る。モデルの出力の有効性・有意性は、予測先行時間だけでなく、適用する時空間スケールに依存する。河川流量の予測（12時間のような非常に短い先行時間）には、その場での観測や、リアルタイムのレーダ雨量、時間外挿の予測レーダ雨量といった観測が利用可能である。先行時間が数日から1週間に延びれば降水量の定量予測は非常に有効になる。ただし、数値モデルの能力が障害であるが。定量予測の不確定性を明確にするため、予測モデルのアンサンブルを用いた。アンサンブルで求める流量予測は、活発な研究分野【まだ研究レベル】であるが、現業は遅れている。改善には4つの項目があげられる。予測モデルの選択と発展、予測降水量の改善、流域モデルの適正化、そして、それらを統合して活用すること。10/25’11 |