小槻 峻司

©2018. The Authors. This study aims to improve precipitation forecasts by estimating model parameters of a numerical weather prediction model with an ensemble-based data assimilation method. We implemented the parameter estimation algorithm into a global atmospheric data assimilation system NICAM-LETKF, which incorporates Nonhydrostatic Icosahedral Atmospheric Model (NICAM) and the Local Ensemble Transform Kalman Filter (LETKF). This study estimated a globally uniform model parameter of a large-scale condensation scheme known as the B1 parameter of Berry's parameterization. We conducted an online estimation of the B1 parameter using the Global Satellite Mapping of Precipitation (GSMaP) data and successfully reduced NICAM's precipitation forecast bias relative to the GSMaP data, especially for weak rains. The estimated B1 parameter evolved toward the optimal value obtained by manual tuning. The parameter estimation also mitigated a dry bias for the lower troposphere in the Tropics. However, the estimated B1 intensified biases for cloud water mixing ratio and outgoing long-wave radiation in the regions where shallow clouds are dominant. This is because only precipitation data were used to estimate the optimal value of B1, and more constraints will be required to obtain a suitable value for climatological simulations.

With the serial treatment of observations in the ensemble Kalman filter (EnKF), the assimilation order of observations is usually assumed to have no significant impact on analysis accuracy. However, Nerger derived that analyses with different assimilation orders are different if covariance localization is applied in the observation space. This study explores whether the assimilation order can be optimized to systematically improve the filter estimates. A mathematical demonstration of a simple two-dimensional case indicates that different assimilation orders can cause different analyses, although the differences are two orders of magnitude smaller than the analysis increments if two identical observation error variances are the same size as the two identical state error variances. Numerical experiments using the Lorenz-96 40-variable model show that the small difference due to different assimilation orders could eventually result in a significant difference in analysis accuracy. Several ordering rules are tested, and the results show that an ordering rule that gives a better forecast relative to future observations improves the analysis accuracy. In addition, the analysis is improved significantly by ordering observations from worse to better impacts using the ensemble forecast sensitivity to observations (EFSO), which estimates how much each observation reduces or increases the forecast error. With the EFSO ordering rule, the change in error during the serial assimilation process is similar to that obtained by the experimentally found best sampled assimilation order. The ordering has more impact when the ensemble size is smaller relative to the degrees of freedom of the dynamical system.

&lt;jats:p&gt;&amp;lt;p&amp;gt;&amp;lt;strong&amp;gt;Abstract.&amp;lt;/strong&amp;gt; To date, many studies have performed numerical estimations of biomass production and agricultural water demand to understand the present and future supply–demand relationship. A crop calendar (CC), which defines the date or month when farmers sow and harvest crops, is an essential input for the numerical estimations. This study aims to present a new global data set, the SAtellite-derived CRop calendar for Agricultural simulations (SACRA), and to discuss advantages and disadvantages compared to existing census-based and model-derived products. We estimate global CC at a spatial resolution of 5 arcmin using satellite-sensed normalized difference vegetation index (NDVI) data, which corresponds to vegetation vitality and senescence on the land surface. Using the time series of the NDVI averaged from three consecutive years (2004–2006), sowing/harvesting dates are estimated for six crops (temperate-wheat, snow-wheat, maize, rice, soybean and cotton). We assume time series of the NDVI represent the phenology of one dominant crop and estimate CCs of the dominant crop in each grid. The dominant crops are determined using harvested areas based on census-based data. The cultivation period of SACRA is identified from the time series of the NDVI; therefore, SACRA considers current effects of human decisions and natural disasters. The difference between the estimated sowing dates and other existing products is less than 2 months (&amp;lt; 62 days) in most of the areas. A major disadvantage of our method is that the mixture of several crops in a grid is not considered in SACRA. The assumption of one dominant crop in each grid is a major source of discrepancy in crop calendars between SACRA and other products. The disadvantages of our approach may be reduced with future improvements based on finer satellite sensors and crop-type classification studies to consider several dominant crops in each grid. The comparison of the CC also demonstrates that identification of wheat type (sowing in spring or fall) is a major source of error in global CC estimations.&amp;lt;/p&amp;gt;<br /> &lt;/jats:p&gt;

In February 2014, the Global Precipitation Measurement (GPM) satellite was launched successfully and started observing global precipitation using the new DPR (Dual-frequency Precipitation Radar) sensor. This study pioneers to compare the GPM/ DPR precipitation products with other satellite-derived products and simulated precipitation from the Nonhydrostatic Icosahedral Atmospheric Model (NICAM) at a 3.5-km horizontal resolution. The NICAM simulation outputs are converted to three-dimensional radar reflectivity using a radar simulator included in the joint simulator for satellite sensors (Joint-Simulator). We focus on the three frontal precipitation cases in the storm track regions, where the NICAM surface precipitation agrees generally well with an existing precipitation product "GSMaP". The surface precipitation patterns, bright band heights and three-dimensional radar reflectivity of GPM/ DPR generally agree with the corresponding variables from GSMaP, NICAM and the Joint-Simulator. However, the radar echo tops of GPM/ DPR are systematically lower than that of NICAM-Joint-Simulator, suggesting that NICAM may overestimate mixing ratios of snow and graupel. The general agreement of surface precipitation patterns between GPM/ DPR and the NICAM simulation encourages a possible use of GPM-derived precipitation data toward numerical weather prediction through data assimilation.

This study was conducted to improve a global crop calendar product using satellite-sensed vegetation indexes. In this study, we newly used EVI and SAVI, in addition to NDVI, to define crop calendar by phenological analysis. Using a global cropping map, parameters to define planting and harvesting dates are determined to three vegetation indexes. Estimated crop calendars using three vegetation indexes agree with a statistical crop calendar at approximately same level. Improvements of global crop calendar using EVI and SAVI are not captured. On the other hand, determining cropping parameters brings about improvement of the crop calendar, which causes an improvement of analyzed agricultural water usage.

&lt;jats:p&gt;In Chao Phraya River basin, the runoff at the middle basin (Nakhon Sawan station: C.2 point) is important for the prevention of lower basin floods. Through analyzing 1980 to 2011 runoff and rain gauge data and performing numerical calculations using a hydrological land surface model, this study will describe a condition that causes massive floods at the C.2 point. The main conclusions are the following: (1) In 2011, precipitation exceeding the average by about 40% caused naturalized runoff +125% (+29 billion m&lt;jats:sup&gt;3&lt;/jats:sup&gt;) that in an average year. The massive 2011 flood would have been difficult to prevent even if the operation of the Bhumibol Dam and Sirikit Dam had been appropriate. (2) In 1980, 1995, and 2006, precipitation exceeding the average by about 10% caused naturalized runoff exceeding that of the average year by 50 to 75%. The runoff rate in the Chao Phraya River basin is about 20%, and characteristically a minor increase in precipitation results in a considerable amount of runoff. (3) There are natural flood years, which have higher than average precipitation that causes massive floods, and there are non-natural flood years, which have high precipitation but nomassive floods. In natural flood years, the precipitation in June, July, and August is higher than that in the average years, and the total water storage capacity is brought close to saturation in September. Due to this, in addition to base runoff, surface runoff increases. (4) The coefficient of the determination of observed runoff from August to October is 0.6481 for rainfall from June to August and 0.5276 for rainfall from August to October. Heavy rainfall in June, July and August has the effect of bringing the soil close to saturation, which is a necessary condition for massive flooding. Massive flooding results if this necessary condition is met and there is heavy rainfall in September and October. This finding is also supported by a high coefficient of determination of 0.7260 between rainfall in May, June, July, August, September, and October and naturalized runoff in August, September, and October.&lt;/jats:p&gt;

&amp;emsp;気候変動が大きな問題となっている現在，その影響を解釈・翻訳し，社会に伝える事は科学の重要な使命である．一連の論文では，日本全域水資源モデルの開発を行い，気候変動が日本全流域の水需給バランスに与える影響を推計する．本稿では，稲成長・水文陸面・灌漑・河道流下・ダム操作の5つのモジュールから成る水資源モデルを提案し，日本全域に適用し検証する．提案する水資源モデルは，従来の水文・水資源分野で扱われてきた水循環の解析に加えて，稲成長や収穫量を解析可能である．観測値を基本とした気象強制力データを用いて，10年間（1994-2003年）の河川流量・米生産量・水ストレス解析を行った．稲成長モジュールにより解析された稲成長（出穂日や収穫日）や収穫量は，都道府県の統計情報によく一致した．国内20の一級河川で解析河川流量を検証し，冬季の降雪量補正が月流量の再現性を改善することを示した．月流量で計算したナッシュの効率係数は，流域別のパラメータ調整を行うことなく，多くの河川で0.8を超えた．CWD指標を用いて解析された日本域の水逼迫度合は，統計情報と同様の傾向を示し，実際の水逼迫を反映することを示した．

&amp;emsp;本研究では，提案する日本全域水資源モデルと，超高解像度GCM（MRI-AGCM3.1S）から出力される現在・近未来・世紀末の気象強制力データを用いて，気候変動が日本の水資源に与える影響を推計した．多雪地域では流況が大きく変わり，12月から3月にかけての流量増加や，融雪時期である4月から5月の流量低下が予測された．東北地方の日本海側では世紀末に水ストレスが強化される流域が多く見られ，世紀末には融雪早期化により多量な水を要する代かきが困難になる可能性が示唆された．世紀末では水資源量増加にも関わらず水ストレスが強化される流域が多く，水資源量の増加が水ストレス緩和に帰結しない事を示した．温暖化が米収穫量に与える影響を推計した結果，北日本・東日本・中日本では温暖化が進むにつれ収量が増加するが，西日本では逆に減少する傾向が見られた．適応策として移植日の変化を検討したところ，北日本・西日本では移植遅延化により収量増加を見込めることを示した．しかし，移植遅延化は北日本の日本海側では水ストレスを強化させる側面も持っており，今後は水ストレスが米収量に与える影響についても評価していく必要がある．

近年，人類の経済発展や人口増加に伴い世界の農業水需要量が急激に増加している。農業水資源計画を策定する上で農業水需要量を知る必要があるが，全球規模での水需要量統計からは，国総量のデータが年単位で得られるのみである。本研究では，i）気象要素を入力条件に，水収支・放射収支・エネルギー収支計算から物理的に解析する事， ii）高い空間・時間解像度で水需要量データを得る事 ，iii）作物による水需要形態の違いを反映する事，を目的に多様な作物分布を考慮した全球農業水需要量の推定を行う。水文陸面過程モデルにより全球灌漑要求水量の推定を行うため，解析に必要となる全球の灌漑パラメータを作成した。作物別の灌漑面積情報は，全球灌漑マップと各国の作物面積統計値とを組み合わせる事により整備し，農事暦をリモートセンシングデータNDVI のフェノロジー解析により作成した。地表植生の活性度を示す植生指標NDVI の時系列情報からは，作物生育の状態を現実的に捉える事が可能である。全球での灌漑要求水量を推定した結果，解析された結果が各国の統計データ及び既往研究の推定値と比較して妥当な値であるとことが示された。地域別にみると，アジア域での米，小麦農地や北アメリカ域の小麦，トウモロコシ農地，ヨーロッパ域の小麦農地で多量の水が消費されている事が分かった。全球耕作地の取水量依存比率（耕作地の年蒸発散量に対する水需要量の比）の比較からは，降水量，土壌物理，作物種が取水量依存度の支配要素である事が分かった。各作物の取水量依存比率を比較した結果，同じ降水量条件下では，米が最も取水量依存比率が高く，綿花が最も低い事が示された。本研究で提案する水需要量推定手法は，農地の土壌物理や気象，作物の違いを反映可能なものであり，流域スケールでの農業水資源計画において有用な情報を提供しうると言える。

In this paper, the particle swarm optimization (PSO) is applied into automatic parameter calibration process of a distributed runoff model. As distributed runoff models require long simulation time compared with general optimization problems, the number of particles and repeat computation times should be selected property. We conducted sensitivity experiments for the number of particles and found that the PSO has to be applied in following conditions: i) to set the number of particles more than 100 in the case of calibrating about five parameters, ii) to conduct repeat computations about 25 times. Analyzed river discharge using identified parameters shows good agreement with the observed one.